Ephedra regional gradient (ERG) analyses

ecoblender

alex.filazzola

Abstract

An under-examined component of the shrub-annual relationship is how regional drivers, such as climate, may alter the sign or magnitude of positive interactions. Interspecific interactions between plants have been shown to be strongly linked to climate, particularly temperature and precipitation. The stress-gradient hypothesis (SGH) predicts that higher abiotic stress (i.e. for deserts, increasing temperature and reduced precipitation) will increase the frequency of positive interactions among shrubs and their annual understory. Regional climate gradients also have indirect effects on plant composition, such as determining consumer abundance and soil nutrient composition. Nutrient availability is particularly affected by precipitation because of altered decomposition rates of organic matter and mineralization. Therefore, the strength of facilitation and operating mechanism of a shrub on the annual plant community may change along a regional gradient.

Hypothesis

We tested the hypothesis that positive interactions among shrubs and annual plants will increase with abiotic stress and reduce nutrient availability along a regional gradient of aridity.

Methods

Weather for growing season

Climate patterns within study

season1.sjd <- season1 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season1.mnp <- season1 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.sjd <- season2 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.mnp <- season2 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))

## Rain vs Temperature in 2016
par(mfrow=c(2,1))
par(mar=c(1.5,4.5,1,4.5))
plot1 <- barplot(height=season1.sjd$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot1[,1], season1.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot1 <- barplot(height=season1.mnp$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
points(plot1[,1], season1.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)

## Rain vs Temperature in 2017
par(mfrow=c(2,1))
par(mar=c(1.5,4.5,1,4.5))
plot2 <- barplot(height=season2.sjd$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot2[,1], season2.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot2 <- barplot(height=season2.mnp$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot2[,1], season2.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)

### 2016 The rain was inconsistent and mostly absent in the Mojave. This resulted in low germination and producitivty at the southern sites
### 2017 The rain was more plentiful, but in the northern sites, there appears to be a frost period after the majority of the rainfall. Need to check number of frost days

season1.frost <- season1 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season1.frost)
##              Site frost.days
## 1         Barstow  24.725275
## 2          Cuyama  29.670330
## 3   HeartofMojave  25.824176
## 4    PanocheHills  10.439560
## 5 SheepholeValley   6.043956
## 6          Tecopa  23.626374
## 7      TejonRanch  50.549451
season2.frost <- season2 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season2.frost)
##              Site frost.days
## 1         Barstow  17.679558
## 2          Cuyama  19.889503
## 3   HeartofMojave  16.574586
## 4    PanocheHills  14.917127
## 5 SheepholeValley   1.785714
## 6          Tecopa  25.966851
## 7      TejonRanch  35.911602
## Both years had comparable number of frost days

## Compare number of consecutive frost days (i.e. frost periods)
season1[,"frost"] <- ifelse(season1$min.temp<0, -99,season1$min.temp) ## identified days below freezing
season2[,"frost"] <- ifelse(season2$min.temp<0, -99,season2$min.temp) ## identified days below freezing
count.consec <- function(x) {max(rle(as.character(x))$lengths)}

season1.frost <- season1 %>% group_by(Site)  %>% summarize(count.consec(frost))
data.frame(season1.frost)
##              Site count.consec.frost.
## 1         Barstow                  10
## 2          Cuyama                  10
## 3   HeartofMojave                  12
## 4    PanocheHills                   5
## 5 SheepholeValley                   7
## 6          Tecopa                  11
## 7      TejonRanch                  14
season2.frost <- season2 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season2.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   7
## 4    PanocheHills                   6
## 5 SheepholeValley                   3
## 6          Tecopa                   7
## 7      TejonRanch                  13
## compare only after plants have germinated
season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100, avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season1.frost)
##              Site frost.days avg.min.temp
## 1         Barstow  12.396694     5.750413
## 2          Cuyama  11.570248     3.352893
## 3   HeartofMojave  16.528926     4.542149
## 4    PanocheHills   2.479339     6.777686
## 5 SheepholeValley   2.479339     9.394167
## 6          Tecopa  11.570248     6.342149
## 7      TejonRanch  33.884298     1.438017
season2.frost <- season2 %>% group_by(Site) %>%  filter(year>2016) %>%  summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100,avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season2.frost)
##              Site frost.days avg.min.temp
## 1         Barstow  11.666667     6.052500
## 2          Cuyama  16.666667     3.624167
## 3   HeartofMojave   8.333333     5.637838
## 4    PanocheHills   8.333333     6.065833
## 5 SheepholeValley   0.000000     9.386916
## 6          Tecopa  20.833333     5.938333
## 7      TejonRanch  28.333333     2.535833
season1.frost <- season1 %>% group_by(Site)  %>% filter(year>2015) %>% summarize(count.consec(frost))
data.frame(season1.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   4
## 4    PanocheHills                   2
## 5 SheepholeValley                   2
## 6          Tecopa                   4
## 7      TejonRanch                  10
season2.frost <- season2 %>% group_by(Site)  %>%  filter(year>2016)%>% summarize(count.consec(frost))
data.frame(season2.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   3
## 4    PanocheHills                   3
## 5 SheepholeValley                   2
## 6          Tecopa                   5
## 7      TejonRanch                  10

Microenvironmental differences

We compared temperature and relative humidity between shrub and open microsites among all sites along the regional gradient


    Shapiro-Wilk normality test

data:  fit1$residuals
W = 0.87881, p-value < 2.2e-16
                     Df Sum Sq Mean Sq F value   Pr(>F)    
Microsite             1   26.5   26.51  59.886 1.24e-14 ***
gradient              6  233.9   38.98  88.065  < 2e-16 ***
Microsite:gradient    6   13.1    2.18   4.927 4.95e-05 ***
Residuals          4290 1898.9    0.44                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
30 observations deleted due to missingness
Analysis of Deviance Table

Model: binomial, link: logit

Response: RH/100

Terms added sequentially (first to last)

                   Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
NULL                                4333     974.99             
Microsite           1     1.33      4332     973.66   0.2492    
gradient            6   528.18      4326     445.48   <2e-16 ***
Microsite:gradient  6     7.69      4320     437.79   0.2613    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table

Model: Gamma, link: inverse

Response: swc

Terms added sequentially (first to last)

               Df Deviance Resid. Df Resid. Dev        F Pr(>F)    
NULL                             404    231.405                    
Site            6  189.683       398     41.722 381.2179 <2e-16 ***
Microsite       1    0.287       397     41.435   3.4579 0.0637 .  
Site:Microsite  6    0.696       391     40.739   1.3994 0.2136    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table

Model: Gamma, link: inverse

Response: swc

Terms added sequentially (first to last)

               Df Deviance Resid. Df Resid. Dev        F Pr(>F)    
NULL                             419    183.193                    
Site            6  144.930       413     38.263 279.4537 <2e-16 ***
Microsite       1    0.080       412     38.183   0.9284 0.3358    
Site:Microsite  6    0.535       406     37.647   1.0322 0.4037    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

site level means

## site coordintes
site.gps <- read.csv("Data/ERGsites.csv")
# nutrient data for each site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
 ## long term climate data extracted from worldclim
clim.dat <- read.csv("Data/ERG.worldclim.csv")


## check correlation among long-term climate variables
cor(clim.dat[,3:8]) ## Temp.wettest.QR least correlated 
##                    Annual.Temp Temp.Seasonality Temp.wettest.QR
## Annual.Temp          1.0000000        0.9051791       0.7260617
## Temp.Seasonality     0.9051791        1.0000000       0.4650548
## Temp.wettest.QR      0.7260617        0.4650548       1.0000000
## Annual.precip       -0.8911069       -0.9650533      -0.5313460
## Precip.seasonality  -0.9715419       -0.9774646      -0.6165170
## Precipt.wettest.QR  -0.8839124       -0.9570957      -0.5523347
##                    Annual.precip Precip.seasonality Precipt.wettest.QR
## Annual.Temp           -0.8911069         -0.9715419         -0.8839124
## Temp.Seasonality      -0.9650533         -0.9774646         -0.9570957
## Temp.wettest.QR       -0.5313460         -0.6165170         -0.5523347
## Annual.precip          1.0000000          0.9613729          0.9980959
## Precip.seasonality     0.9613729          1.0000000          0.9543657
## Precipt.wettest.QR     0.9980959          0.9543657          1.0000000
## Obtain mean shrub traits for each site
shrubs <- read.csv("Data/ERG.shrub.csv")
shrubs <- subset(shrubs, Microsite=="shrub")
shrubs.mean <- shrubs %>% group_by(Gradient,Site) %>% summarise_all(funs(mean))
shrubs.mean <- data.frame(shrubs.mean)
shrubs.vars <- shrubs.mean[,c("Site","Gradient","volume","canopy","Dx","DxEph","Compaction")] 

## test correlation among shrub traits
cor(shrubs.vars[,3:7]) ## compaction x volume & DxEph x Dx high correlation
##                volume     canopy          Dx      DxEph  Compaction
## volume      1.0000000  0.2243902  0.40036651  0.2631563 -0.64927168
## canopy      0.2243902  1.0000000 -0.68479570 -0.3538094 -0.20833287
## Dx          0.4003665 -0.6847957  1.00000000  0.7803527 -0.06531485
## DxEph       0.2631563 -0.3538094  0.78035272  1.0000000  0.33769574
## Compaction -0.6492717 -0.2083329 -0.06531485  0.3376957  1.00000000
vifstep(shrubs.vars[,3:7], th=10) ## Dx showing collinearity problems
## 1 variables from the 5 input variables have collinearity problem: 
##  
## Dx 
## 
## After excluding the collinear variables, the linear correlation coefficients ranges between: 
## min correlation ( Compaction ~ canopy ):  -0.2083329 
## max correlation ( Compaction ~ volume ):  -0.6492717 
## 
## ---------- VIFs of the remained variables -------- 
##    Variables      VIF
## 1     volume 4.043473
## 2     canopy 1.474526
## 3      DxEph 2.874621
## 4 Compaction 3.760284
shrub.site <- shrubs.vars[,-c(1,2,5)]
rownames(shrub.site) <- shrubs.vars[,1]

## PCA of shrub characteristics
pca1 <- prcomp(log(shrub.site), scale=T)
plot(pca1)

biplot(pca1, scale = T)

summary(pca1) ## 77% variation explained
## Importance of components:
##                           PC1    PC2    PC3     PC4
## Standard deviation     1.2958 1.1879 0.8922 0.33722
## Proportion of Variance 0.4198 0.3528 0.1990 0.02843
## Cumulative Proportion  0.4198 0.7725 0.9716 1.00000
## PCA of site characteristics for season1
## Obtain mean weather variables for each site
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T))
season1.mean  <- data.frame(season1.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress

##combine nutrients and long-term averages
site.vars <- data.frame(season1.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations, 
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)
##              temp.var     Precip        wind  elevation
## temp.var   1.00000000 -0.8679054  0.05816084 -0.2699588
## Precip    -0.86790537  1.0000000 -0.37279135  0.1222504
## wind       0.05816084 -0.3727914  1.00000000  0.1054497
## elevation -0.26995879  0.1222504  0.10544974  1.0000000
site.vars2016 <- site.vars

##  check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum
## No variable from the 4 input variables has collinearity problem. 
## 
## The linear correlation coefficients ranges between: 
## min correlation ( wind ~ temp.var ):  0.05816084 
## max correlation ( Precip ~ temp.var ):  -0.8679054 
## 
## ---------- VIFs of the remained variables -------- 
##   Variables      VIF
## 1  temp.var 6.640710
## 2    Precip 7.316232
## 3      wind 1.739558
## 4 elevation 1.142681
pca1 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca1)

biplot(pca1)

## check contribution of loadings
pca1$rotation
##                  PC1        PC2         PC3        PC4
## temp.var   0.6001002 -0.1669018  0.50554765 -0.5970303
## Precip    -0.6703313 -0.1105034 -0.09936844 -0.7270288
## wind       0.2743589  0.7981791 -0.43410209 -0.3149488
## elevation -0.3395039  0.5681927  0.73898773  0.1256633
aload <- abs(pca1$rotation)
sweep(aload, 2, colSums(aload), "/")
##                 PC1       PC2        PC3        PC4
## temp.var  0.3184748 0.1015355 0.28433406 0.33832382
## Precip    0.3557466 0.0672253 0.05588758 0.41199111
## wind      0.1456030 0.4855763 0.24415109 0.17847449
## elevation 0.1801756 0.3456629 0.41562726 0.07121058
summary(pca1) ## 89% variation explained
## Importance of components:
##                          PC1    PC2    PC3     PC4
## Standard deviation     1.456 1.0458 0.8577 0.22479
## Proportion of Variance 0.530 0.2734 0.1839 0.01263
## Cumulative Proportion  0.530 0.8034 0.9874 1.00000
gradient1.season1 <- pca1$x[,1]
gradient2.season1 <- pca1$x[,2]

## season2
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T))
season2.mean  <- data.frame(season2.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress

##combine nutrients and long-term averages
site.vars <- data.frame(season2.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations, 
row.names(site.vars) <-  shrubs.vars[,1]
cor(site.vars)
##             temp.var     Precip       wind  elevation
## temp.var   1.0000000 -0.7427028 -0.2262409 -0.3715471
## Precip    -0.7427028  1.0000000 -0.1551517  0.1813600
## wind      -0.2262409 -0.1551517  1.0000000  0.1052805
## elevation -0.3715471  0.1813600  0.1052805  1.0000000
site.vars2017 <- site.vars


##  check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum
## No variable from the 4 input variables has collinearity problem. 
## 
## The linear correlation coefficients ranges between: 
## min correlation ( elevation ~ wind ):  0.1052805 
## max correlation ( Precip ~ temp.var ):  -0.7427028 
## 
## ---------- VIFs of the remained variables -------- 
##   Variables      VIF
## 1  temp.var 3.438786
## 2    Precip 3.035337
## 3      wind 1.402010
## 4 elevation 1.192010
pca2 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca2)

biplot(pca2)

summary(pca2) ## 85% variation explained
## Importance of components:
##                          PC1    PC2    PC3     PC4
## Standard deviation     1.434 1.0121 0.8809 0.37883
## Proportion of Variance 0.514 0.2561 0.1940 0.03588
## Cumulative Proportion  0.514 0.7701 0.9641 1.00000
## check contribution of loadings
pca2$rotation
##                  PC1         PC2         PC3       PC4
## temp.var   0.6557532 -0.09123357  0.20565681 0.7206729
## Precip    -0.6176537 -0.16891771 -0.40105332 0.6550778
## wind      -0.1019884  0.97279862  0.06826009 0.1964733
## elevation -0.4220070 -0.12963831  0.89005734 0.1135866
aload <- abs(pca2$rotation)
sweep(aload, 2, colSums(aload), "/")
##                  PC1        PC2       PC3        PC4
## temp.var  0.36483385 0.06695609 0.1314078 0.42749339
## Precip    0.34363685 0.12396828 0.2562596 0.38858328
## wind      0.05674213 0.71393442 0.0436159 0.11654531
## elevation 0.23478717 0.09514122 0.5687167 0.06737801
## define gradients
gradient1.season2 <- pca2$x[,1]
gradient2.season2 <- pca2$x[,2]

Compare gradient to phytometer

# crs.world <-CRS("+proj=longlat +datum=WGS84")
# gps <- data.frame(x=site.gps$long,y=site.gps$lat)
# coordinates(gps) <- ~x+y
# proj4string(gps) <- crs.world
# 
# ## Download and extract PET/aridity
# r1 <- raster("C:\\Users\\Alessandro\\Downloads\\Global Aridity - Annual\\AI_annual\\ai_yr\\w001001.adf", package="raster") #aridity
# r2 <- raster("C:\\Users\\Alessandro\\Downloads\\Global PET - Annual\\PET_he_annual\\pet_he_yr\\w001001.adf", package="raster") #potential evapotranspiration
# calclim <- stack(r1,r2)
# names(calclim) <- c("aridity","PET")
# arid.vals <- raster::extract(calclim, gps)
# rownames(arid.vals) <- site.gps[,"name"]
# colnames(arid.vals)[1] <- "Gradient"
# write.csv(arid.vals, "Data/aridity.PET.csv")
arid.vals <- read.csv("Data/aridity.PET.csv")
colnames(arid.vals)[1] <- "Site"

mean.phyto <- census %>% filter(Census=="end") %>%group_by(Year, Gradient, Site, Microsite) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
mean.phyto <- data.frame(mean.phyto)

rii.data <- rii(census, 1:6, var=9:15)
##      Year    Census            Site Rep Gradient Microsite    Phacelia
## 1    2016       end    PanocheHills   1        1     shrub  0.28571429
## 3    2016       end    PanocheHills   2        1     shrub  0.60000000
## 5    2016       end    PanocheHills   3        1     shrub -0.14285714
## 7    2016       end    PanocheHills   4        1     shrub -0.25000000
## 9    2016       end    PanocheHills   5        1     shrub  1.00000000
## 11   2016       end    PanocheHills   6        1     shrub  0.50000000
## 13   2016       end    PanocheHills   7        1     shrub  1.00000000
## 15   2016       end    PanocheHills   8        1     shrub  0.75000000
## 17   2016       end    PanocheHills   9        1     shrub  0.41176471
## 19   2016       end    PanocheHills  10        1     shrub  0.38461538
## 21   2016       end    PanocheHills  11        1     shrub  0.00000000
## 23   2016       end    PanocheHills  12        1     shrub  1.00000000
## 25   2016       end    PanocheHills  13        1     shrub -0.60000000
## 27   2016       end    PanocheHills  14        1     shrub  0.00000000
## 29   2016       end    PanocheHills  15        1     shrub  0.69230769
## 31   2016       end    PanocheHills  16        1     shrub  0.45454545
## 33   2016       end    PanocheHills  17        1     shrub -1.00000000
## 35   2016       end    PanocheHills  18        1     shrub  0.27272727
## 37   2016       end    PanocheHills  19        1     shrub -1.00000000
## 39   2016       end    PanocheHills  20        1     shrub  1.00000000
## 41   2016       end    PanocheHills  21        1     shrub  1.00000000
## 43   2016       end    PanocheHills  22        1     shrub  0.60000000
## 45   2016       end    PanocheHills  23        1     shrub  1.00000000
## 47   2016       end    PanocheHills  24        1     shrub -0.81818182
## 49   2016       end    PanocheHills  25        1     shrub  1.00000000
## 51   2016       end    PanocheHills  26        1     shrub  1.00000000
## 53   2016       end    PanocheHills  27        1     shrub  1.00000000
## 55   2016       end    PanocheHills  28        1     shrub  1.00000000
## 57   2016       end    PanocheHills  29        1     shrub  1.00000000
## 59   2016       end    PanocheHills  30        1     shrub -0.33333333
## 61   2016       end          Cuyama   1        2     shrub  0.33333333
## 63   2016       end          Cuyama   2        2     shrub -1.00000000
## 65   2016       end          Cuyama   3        2     shrub  0.00000000
## 67   2016       end          Cuyama   4        2     shrub  1.00000000
## 69   2016       end          Cuyama   5        2     shrub  0.33333333
## 71   2016       end          Cuyama   6        2     shrub  1.00000000
## 73   2016       end          Cuyama   7        2     shrub -1.00000000
## 75   2016       end          Cuyama   8        2     shrub  0.00000000
## 77   2016       end          Cuyama   9        2     shrub -1.00000000
## 79   2016       end          Cuyama  10        2     shrub  0.50000000
## 81   2016       end          Cuyama  11        2     shrub  1.00000000
## 83   2016       end          Cuyama  12        2     shrub  0.00000000
## 85   2016       end          Cuyama  13        2     shrub -1.00000000
## 87   2016       end          Cuyama  14        2     shrub  0.00000000
## 89   2016       end          Cuyama  15        2     shrub  0.00000000
## 91   2016       end          Cuyama  16        2     shrub  1.00000000
## 93   2016       end          Cuyama  17        2     shrub -1.00000000
## 95   2016       end          Cuyama  18        2     shrub  1.00000000
## 97   2016       end          Cuyama  19        2     shrub  0.00000000
## 99   2016       end          Cuyama  20        2     shrub  1.00000000
## 101  2016       end          Cuyama  21        2     shrub  1.00000000
## 103  2016       end          Cuyama  22        2     shrub  1.00000000
## 105  2016       end          Cuyama  23        2     shrub -1.00000000
## 107  2016       end          Cuyama  24        2     shrub -1.00000000
## 109  2016       end          Cuyama  25        2     shrub  0.00000000
## 111  2016       end          Cuyama  26        2     shrub  1.00000000
## 113  2016       end          Cuyama  27        2     shrub -0.40000000
## 115  2016       end          Cuyama  28        2     shrub  0.33333333
## 117  2016       end          Cuyama  29        2     shrub -0.33333333
## 119  2016       end          Cuyama  30        2     shrub  0.00000000
## 121  2016       end         Barstow   1        4     shrub  0.00000000
## 123  2016       end         Barstow   2        4     shrub  0.00000000
## 125  2016       end         Barstow   3        4     shrub  0.00000000
## 127  2016       end         Barstow   4        4     shrub -1.00000000
## 129  2016       end         Barstow   5        4     shrub  0.00000000
## 131  2016       end         Barstow   6        4     shrub  0.00000000
## 133  2016       end         Barstow   7        4     shrub  0.00000000
## 135  2016       end         Barstow   8        4     shrub  0.00000000
## 137  2016       end         Barstow   9        4     shrub  0.00000000
## 139  2016       end         Barstow  10        4     shrub  0.00000000
## 141  2016       end         Barstow  11        4     shrub  0.00000000
## 143  2016       end         Barstow  12        4     shrub  0.00000000
## 145  2016       end         Barstow  13        4     shrub  0.00000000
## 147  2016       end         Barstow  14        4     shrub  0.00000000
## 149  2016       end         Barstow  15        4     shrub  0.00000000
## 151  2016       end         Barstow  16        4     shrub -1.00000000
## 153  2016       end         Barstow  17        4     shrub  0.00000000
## 155  2016       end         Barstow  18        4     shrub  1.00000000
## 157  2016       end         Barstow  19        4     shrub  0.00000000
## 159  2016       end         Barstow  20        4     shrub  0.00000000
## 161  2016       end         Barstow  21        4     shrub  1.00000000
## 163  2016       end         Barstow  22        4     shrub  0.00000000
## 165  2016       end         Barstow  23        4     shrub  0.00000000
## 167  2016       end         Barstow  24        4     shrub  0.00000000
## 169  2016       end         Barstow  25        4     shrub  0.00000000
## 171  2016       end         Barstow  26        4     shrub  0.00000000
## 173  2016       end         Barstow  27        4     shrub  0.00000000
## 175  2016       end         Barstow  28        4     shrub  0.00000000
## 177  2016       end         Barstow  29        4     shrub  0.00000000
## 179  2016       end         Barstow  30        4     shrub  0.00000000
## 181  2016       end   HeartofMojave   1        5     shrub  0.60000000
## 183  2016       end   HeartofMojave   2        5     shrub  1.00000000
## 185  2016       end   HeartofMojave   3        5     shrub  0.00000000
## 187  2016       end   HeartofMojave   4        5     shrub  1.00000000
## 189  2016       end   HeartofMojave   5        5     shrub  0.00000000
## 191  2016       end   HeartofMojave   6        5     shrub  1.00000000
## 193  2016       end   HeartofMojave   7        5     shrub  0.00000000
## 195  2016       end   HeartofMojave   8        5     shrub -1.00000000
## 197  2016       end   HeartofMojave   9        5     shrub -1.00000000
## 199  2016       end   HeartofMojave  10        5     shrub -0.42857143
## 201  2016       end   HeartofMojave  11        5     shrub -0.14285714
## 203  2016       end   HeartofMojave  12        5     shrub  0.25000000
## 205  2016       end   HeartofMojave  13        5     shrub -0.20000000
## 207  2016       end   HeartofMojave  14        5     shrub  1.00000000
## 209  2016       end   HeartofMojave  15        5     shrub -1.00000000
## 211  2016       end   HeartofMojave  16        5     shrub  0.00000000
## 213  2016       end   HeartofMojave  17        5     shrub  1.00000000
## 215  2016       end   HeartofMojave  18        5     shrub -0.33333333
## 217  2016       end   HeartofMojave  19        5     shrub -0.66666667
## 219  2016       end   HeartofMojave  20        5     shrub  1.00000000
## 221  2016       end   HeartofMojave  21        5     shrub -1.00000000
## 223  2016       end   HeartofMojave  22        5     shrub  0.20000000
## 225  2016       end   HeartofMojave  23        5     shrub  1.00000000
## 227  2016       end   HeartofMojave  24        5     shrub  0.00000000
## 229  2016       end   HeartofMojave  25        5     shrub -0.33333333
## 231  2016       end   HeartofMojave  26        5     shrub  0.00000000
## 233  2016       end   HeartofMojave  27        5     shrub  1.00000000
## 235  2016       end   HeartofMojave  28        5     shrub  0.00000000
## 237  2016       end   HeartofMojave  29        5     shrub  1.00000000
## 239  2016       end   HeartofMojave  30        5     shrub  0.80000000
## 241  2016       end SheepholeValley   1        6     shrub  1.00000000
## 243  2016       end SheepholeValley   2        6     shrub  0.00000000
## 245  2016       end SheepholeValley   3        6     shrub -1.00000000
## 247  2016       end SheepholeValley   4        6     shrub  0.00000000
## 249  2016       end SheepholeValley   5        6     shrub  0.00000000
## 251  2016       end SheepholeValley   6        6     shrub  1.00000000
## 253  2016       end SheepholeValley   7        6     shrub  0.00000000
## 255  2016       end SheepholeValley   8        6     shrub  0.00000000
## 257  2016       end SheepholeValley   9        6     shrub  0.00000000
## 259  2016       end SheepholeValley  10        6     shrub  0.00000000
## 261  2016       end SheepholeValley  11        6     shrub  0.00000000
## 263  2016       end SheepholeValley  12        6     shrub  0.00000000
## 265  2016       end SheepholeValley  13        6     shrub  0.00000000
## 267  2016       end SheepholeValley  14        6     shrub  0.00000000
## 269  2016       end SheepholeValley  15        6     shrub  0.00000000
## 271  2016       end SheepholeValley  16        6     shrub  0.00000000
## 273  2016       end SheepholeValley  17        6     shrub  0.00000000
## 275  2016       end SheepholeValley  18        6     shrub  0.00000000
## 277  2016       end SheepholeValley  19        6     shrub  0.00000000
## 279  2016       end SheepholeValley  20        6     shrub  1.00000000
## 281  2016       end SheepholeValley  21        6     shrub  0.00000000
## 283  2016       end SheepholeValley  22        6     shrub  0.00000000
## 285  2016       end SheepholeValley  23        6     shrub  0.00000000
## 287  2016       end SheepholeValley  24        6     shrub  0.00000000
## 289  2016       end SheepholeValley  25        6     shrub  0.00000000
## 291  2016       end SheepholeValley  26        6     shrub  0.00000000
## 293  2016       end SheepholeValley  27        6     shrub  0.00000000
## 295  2016       end SheepholeValley  28        6     shrub  0.00000000
## 297  2016       end SheepholeValley  29        6     shrub  0.00000000
## 299  2016       end SheepholeValley  30        6     shrub  0.00000000
## 301  2016       end          Tecopa   1        7     shrub -1.00000000
## 303  2016       end          Tecopa   2        7     shrub  1.00000000
## 305  2016       end          Tecopa   3        7     shrub  0.00000000
## 307  2016       end          Tecopa   4        7     shrub  1.00000000
## 309  2016       end          Tecopa   5        7     shrub -0.14285714
## 311  2016       end          Tecopa   6        7     shrub  1.00000000
## 313  2016       end          Tecopa   7        7     shrub  0.00000000
## 315  2016       end          Tecopa   8        7     shrub  0.00000000
## 317  2016       end          Tecopa   9        7     shrub  0.00000000
## 319  2016       end          Tecopa  10        7     shrub  0.33333333
## 321  2016       end          Tecopa  11        7     shrub  1.00000000
## 323  2016       end          Tecopa  12        7     shrub  0.00000000
## 325  2016       end          Tecopa  13        7     shrub  0.00000000
## 327  2016       end          Tecopa  14        7     shrub -1.00000000
## 329  2016       end          Tecopa  15        7     shrub  0.00000000
## 331  2016       end          Tecopa  16        7     shrub  0.00000000
## 333  2016       end          Tecopa  17        7     shrub  0.00000000
## 335  2016       end          Tecopa  18        7     shrub -1.00000000
## 337  2016       end          Tecopa  19        7     shrub  0.00000000
## 339  2016       end          Tecopa  20        7     shrub  0.00000000
## 341  2016       end          Tecopa  21        7     shrub  1.00000000
## 343  2016       end          Tecopa  22        7     shrub  1.00000000
## 345  2016       end          Tecopa  23        7     shrub  0.00000000
## 347  2016       end          Tecopa  24        7     shrub  0.00000000
## 349  2016       end          Tecopa  25        7     shrub  0.00000000
## 351  2016       end          Tecopa  26        7     shrub  1.00000000
## 353  2016       end          Tecopa  27        7     shrub  0.00000000
## 355  2016       end          Tecopa  28        7     shrub  0.00000000
## 357  2016       end          Tecopa  29        7     shrub  0.00000000
## 359  2016       end          Tecopa  30        7     shrub  0.00000000
## 361  2016       end      TejonRanch   1        3     shrub  0.00000000
## 363  2016       end      TejonRanch   2        3     shrub  0.00000000
## 365  2016       end      TejonRanch   3        3     shrub  0.00000000
## 367  2016       end      TejonRanch   4        3     shrub  0.00000000
## 369  2016       end      TejonRanch   5        3     shrub -1.00000000
## 371  2016       end      TejonRanch   6        3     shrub  0.00000000
## 373  2016       end      TejonRanch   7        3     shrub  0.00000000
## 375  2016       end      TejonRanch   8        3     shrub  0.00000000
## 377  2016       end      TejonRanch   9        3     shrub  1.00000000
## 379  2016       end      TejonRanch  10        3     shrub  0.00000000
## 381  2016       end      TejonRanch  11        3     shrub  0.00000000
## 383  2016       end      TejonRanch  12        3     shrub  0.00000000
## 385  2016       end      TejonRanch  13        3     shrub  0.00000000
## 387  2016       end      TejonRanch  14        3     shrub  0.00000000
## 389  2016       end      TejonRanch  15        3     shrub  0.00000000
## 391  2016       end      TejonRanch  16        3     shrub  0.00000000
## 393  2016       end      TejonRanch  17        3     shrub  1.00000000
## 395  2016       end      TejonRanch  18        3     shrub  0.00000000
## 397  2016       end      TejonRanch  19        3     shrub  0.00000000
## 399  2016       end      TejonRanch  20        3     shrub  0.00000000
## 401  2016       end      TejonRanch  21        3     shrub  0.00000000
## 403  2016       end      TejonRanch  22        3     shrub  0.00000000
## 405  2016       end      TejonRanch  23        3     shrub  0.00000000
## 407  2016       end      TejonRanch  24        3     shrub -1.00000000
## 409  2016       end      TejonRanch  25        3     shrub  0.00000000
## 411  2016       end      TejonRanch  26        3     shrub  0.00000000
## 413  2016       end      TejonRanch  27        3     shrub  0.00000000
## 415  2016       end      TejonRanch  28        3     shrub  0.00000000
## 417  2016       end      TejonRanch  29        3     shrub  0.00000000
## 419  2016       end      TejonRanch  30        3     shrub  0.00000000
## 421  2016 emergence    PanocheHills   1        1     shrub -0.42857143
## 423  2016 emergence    PanocheHills   2        1     shrub  0.60000000
## 425  2016 emergence    PanocheHills   3        1     shrub  0.14285714
## 427  2016 emergence    PanocheHills   4        1     shrub -0.27272727
## 429  2016 emergence    PanocheHills   5        1     shrub  0.50000000
## 431  2016 emergence    PanocheHills   6        1     shrub  0.55555556
## 433  2016 emergence    PanocheHills   7        1     shrub  0.00000000
## 435  2016 emergence    PanocheHills   8        1     shrub  0.66666667
## 437  2016 emergence    PanocheHills   9        1     shrub  0.61904762
## 439  2016 emergence    PanocheHills  10        1     shrub  0.37500000
## 441  2016 emergence    PanocheHills  11        1     shrub  0.00000000
## 443  2016 emergence    PanocheHills  12        1     shrub  0.00000000
## 445  2016 emergence    PanocheHills  13        1     shrub -0.42857143
## 447  2016 emergence    PanocheHills  14        1     shrub  0.05882353
## 449  2016 emergence    PanocheHills  15        1     shrub  0.66666667
## 451  2016 emergence    PanocheHills  16        1     shrub  0.40000000
## 453  2016 emergence    PanocheHills  17        1     shrub  0.50000000
## 455  2016 emergence    PanocheHills  18        1     shrub  0.66666667
## 457  2016 emergence    PanocheHills  19        1     shrub  0.20000000
## 459  2016 emergence    PanocheHills  20        1     shrub  0.00000000
## 461  2016 emergence    PanocheHills  21        1     shrub  0.00000000
## 463  2016 emergence    PanocheHills  22        1     shrub -0.66666667
## 465  2016 emergence    PanocheHills  23        1     shrub  0.00000000
## 467  2016 emergence    PanocheHills  24        1     shrub -0.75000000
## 469  2016 emergence    PanocheHills  25        1     shrub  0.75000000
## 471  2016 emergence    PanocheHills  26        1     shrub  0.00000000
## 473  2016 emergence    PanocheHills  27        1     shrub  0.84615385
## 475  2016 emergence    PanocheHills  28        1     shrub  0.66666667
## 477  2016 emergence    PanocheHills  29        1     shrub  0.50000000
## 479  2016 emergence    PanocheHills  30        1     shrub  0.33333333
## 481  2016 emergence          Cuyama   1        2     shrub -1.00000000
## 483  2016 emergence          Cuyama   2        2     shrub -1.00000000
## 485  2016 emergence          Cuyama   3        2     shrub  0.00000000
## 487  2016 emergence          Cuyama   4        2     shrub  0.42857143
## 489  2016 emergence          Cuyama   5        2     shrub -0.42857143
## 491  2016 emergence          Cuyama   6        2     shrub -0.20000000
## 493  2016 emergence          Cuyama   7        2     shrub -0.42857143
## 495  2016 emergence          Cuyama   8        2     shrub  1.00000000
## 497  2016 emergence          Cuyama   9        2     shrub -0.60000000
## 499  2016 emergence          Cuyama  10        2     shrub -0.11111111
## 501  2016 emergence          Cuyama  11        2     shrub  1.00000000
## 503  2016 emergence          Cuyama  12        2     shrub  1.00000000
## 505  2016 emergence          Cuyama  13        2     shrub -1.00000000
## 507  2016 emergence          Cuyama  14        2     shrub  1.00000000
## 509  2016 emergence          Cuyama  15        2     shrub -1.00000000
## 511  2016 emergence          Cuyama  16        2     shrub  1.00000000
## 513  2016 emergence          Cuyama  17        2     shrub -1.00000000
## 515  2016 emergence          Cuyama  18        2     shrub -1.00000000
## 517  2016 emergence          Cuyama  19        2     shrub -1.00000000
## 519  2016 emergence          Cuyama  20        2     shrub  0.00000000
## 521  2016 emergence          Cuyama  21        2     shrub  1.00000000
## 523  2016 emergence          Cuyama  22        2     shrub -0.33333333
## 525  2016 emergence          Cuyama  23        2     shrub -1.00000000
## 527  2016 emergence          Cuyama  24        2     shrub  1.00000000
## 529  2016 emergence          Cuyama  25        2     shrub -1.00000000
## 531  2016 emergence          Cuyama  26        2     shrub -0.20000000
## 533  2016 emergence          Cuyama  27        2     shrub -0.40000000
## 535  2016 emergence          Cuyama  28        2     shrub  0.42857143
## 537  2016 emergence          Cuyama  29        2     shrub -0.77777778
## 539  2016 emergence          Cuyama  30        2     shrub  0.00000000
## 541  2016 emergence         Barstow   1        4     shrub  0.00000000
## 543  2016 emergence         Barstow   2        4     shrub -0.27272727
## 545  2016 emergence         Barstow   3        4     shrub  0.00000000
## 547  2016 emergence         Barstow   4        4     shrub -0.60000000
## 549  2016 emergence         Barstow   5        4     shrub -0.25000000
## 551  2016 emergence         Barstow   6        4     shrub -0.66666667
## 553  2016 emergence         Barstow   7        4     shrub  0.71428571
## 555  2016 emergence         Barstow   8        4     shrub  0.00000000
## 557  2016 emergence         Barstow   9        4     shrub  0.33333333
## 559  2016 emergence         Barstow  10        4     shrub  0.00000000
## 561  2016 emergence         Barstow  11        4     shrub  0.77777778
## 563  2016 emergence         Barstow  12        4     shrub  0.00000000
## 565  2016 emergence         Barstow  13        4     shrub  0.00000000
## 567  2016 emergence         Barstow  14        4     shrub  0.80000000
## 569  2016 emergence         Barstow  15        4     shrub  0.00000000
## 571  2016 emergence         Barstow  16        4     shrub -0.33333333
## 573  2016 emergence         Barstow  17        4     shrub  0.00000000
## 575  2016 emergence         Barstow  18        4     shrub  0.00000000
## 577  2016 emergence         Barstow  19        4     shrub  0.00000000
## 579  2016 emergence         Barstow  20        4     shrub  0.00000000
## 581  2016 emergence         Barstow  21        4     shrub  0.00000000
## 583  2016 emergence         Barstow  22        4     shrub  0.71428571
## 585  2016 emergence         Barstow  23        4     shrub  0.00000000
## 587  2016 emergence         Barstow  24        4     shrub  0.00000000
## 589  2016 emergence         Barstow  25        4     shrub  0.00000000
## 591  2016 emergence         Barstow  26        4     shrub  0.00000000
## 593  2016 emergence         Barstow  27        4     shrub  0.00000000
## 595  2016 emergence         Barstow  28        4     shrub  0.00000000
## 597  2016 emergence         Barstow  29        4     shrub  0.20000000
## 599  2016 emergence         Barstow  30        4     shrub  0.33333333
## 601  2016 emergence   HeartofMojave   1        5     shrub -0.50000000
## 603  2016 emergence   HeartofMojave   2        5     shrub -0.25000000
## 605  2016 emergence   HeartofMojave   3        5     shrub  0.00000000
## 607  2016 emergence   HeartofMojave   4        5     shrub -0.12500000
## 609  2016 emergence   HeartofMojave   5        5     shrub  0.38461538
## 611  2016 emergence   HeartofMojave   6        5     shrub -0.50000000
## 613  2016 emergence   HeartofMojave   7        5     shrub -0.14285714
## 615  2016 emergence   HeartofMojave   8        5     shrub -0.77777778
## 617  2016 emergence   HeartofMojave   9        5     shrub  0.14285714
## 619  2016 emergence   HeartofMojave  10        5     shrub -0.25000000
## 621  2016 emergence   HeartofMojave  11        5     shrub -0.50000000
## 623  2016 emergence   HeartofMojave  12        5     shrub  0.20000000
## 625  2016 emergence   HeartofMojave  13        5     shrub -0.60000000
## 627  2016 emergence   HeartofMojave  14        5     shrub  0.00000000
## 629  2016 emergence   HeartofMojave  15        5     shrub  0.00000000
## 631  2016 emergence   HeartofMojave  16        5     shrub -0.62500000
## 633  2016 emergence   HeartofMojave  17        5     shrub -0.25000000
## 635  2016 emergence   HeartofMojave  18        5     shrub -0.20000000
## 637  2016 emergence   HeartofMojave  19        5     shrub -0.83333333
## 639  2016 emergence   HeartofMojave  20        5     shrub -0.75000000
## 641  2016 emergence   HeartofMojave  21        5     shrub -0.66666667
## 643  2016 emergence   HeartofMojave  22        5     shrub  0.83333333
## 645  2016 emergence   HeartofMojave  23        5     shrub  0.00000000
## 647  2016 emergence   HeartofMojave  24        5     shrub  0.73333333
## 649  2016 emergence   HeartofMojave  25        5     shrub -0.46666667
## 651  2016 emergence   HeartofMojave  26        5     shrub -0.33333333
## 653  2016 emergence   HeartofMojave  27        5     shrub  0.00000000
## 655  2016 emergence   HeartofMojave  28        5     shrub  0.00000000
## 657  2016 emergence   HeartofMojave  29        5     shrub  0.00000000
## 659  2016 emergence   HeartofMojave  30        5     shrub  0.00000000
## 661  2016 emergence SheepholeValley   1        6     shrub -0.40740741
## 663  2016 emergence SheepholeValley   2        6     shrub  0.25000000
## 665  2016 emergence SheepholeValley   3        6     shrub  0.00000000
## 667  2016 emergence SheepholeValley   4        6     shrub -0.33333333
## 669  2016 emergence SheepholeValley   5        6     shrub  0.00000000
## 671  2016 emergence SheepholeValley   6        6     shrub  0.00000000
## 673  2016 emergence SheepholeValley   7        6     shrub  0.00000000
## 675  2016 emergence SheepholeValley   8        6     shrub -0.36842105
## 677  2016 emergence SheepholeValley   9        6     shrub  0.71428571
## 679  2016 emergence SheepholeValley  10        6     shrub  0.00000000
## 681  2016 emergence SheepholeValley  11        6     shrub  0.00000000
## 683  2016 emergence SheepholeValley  12        6     shrub  0.00000000
## 685  2016 emergence SheepholeValley  13        6     shrub  0.00000000
## 687  2016 emergence SheepholeValley  14        6     shrub  0.00000000
## 689  2016 emergence SheepholeValley  15        6     shrub  0.00000000
## 691  2016 emergence SheepholeValley  16        6     shrub  0.00000000
## 693  2016 emergence SheepholeValley  17        6     shrub  0.00000000
## 695  2016 emergence SheepholeValley  18        6     shrub  0.00000000
## 697  2016 emergence SheepholeValley  19        6     shrub  0.00000000
## 699  2016 emergence SheepholeValley  20        6     shrub  0.28000000
## 701  2016 emergence SheepholeValley  21        6     shrub  0.00000000
## 703  2016 emergence SheepholeValley  22        6     shrub  0.00000000
## 705  2016 emergence SheepholeValley  23        6     shrub  0.00000000
## 707  2016 emergence SheepholeValley  24        6     shrub  0.00000000
## 709  2016 emergence SheepholeValley  25        6     shrub  0.00000000
## 711  2016 emergence SheepholeValley  26        6     shrub -0.75000000
## 713  2016 emergence SheepholeValley  27        6     shrub  0.00000000
## 715  2016 emergence SheepholeValley  28        6     shrub  0.00000000
## 717  2016 emergence SheepholeValley  29        6     shrub  0.00000000
## 719  2016 emergence SheepholeValley  30        6     shrub  0.00000000
## 721  2016 emergence          Tecopa   1        7     shrub  0.00000000
## 723  2016 emergence          Tecopa   2        7     shrub  0.00000000
## 725  2016 emergence          Tecopa   3        7     shrub  0.00000000
## 727  2016 emergence          Tecopa   4        7     shrub  0.00000000
## 729  2016 emergence          Tecopa   5        7     shrub  0.00000000
## 731  2016 emergence          Tecopa   6        7     shrub  0.00000000
## 733  2016 emergence          Tecopa   7        7     shrub  0.66666667
## 735  2016 emergence          Tecopa   8        7     shrub -0.60000000
## 737  2016 emergence          Tecopa   9        7     shrub  0.00000000
## 739  2016 emergence          Tecopa  10        7     shrub  0.50000000
## 741  2016 emergence          Tecopa  11        7     shrub  0.00000000
## 743  2016 emergence          Tecopa  12        7     shrub  0.00000000
## 745  2016 emergence          Tecopa  13        7     shrub  0.00000000
## 747  2016 emergence          Tecopa  14        7     shrub  0.00000000
## 749  2016 emergence          Tecopa  15        7     shrub  0.00000000
## 751  2016 emergence          Tecopa  16        7     shrub  0.71428571
## 753  2016 emergence          Tecopa  17        7     shrub  0.00000000
## 755  2016 emergence          Tecopa  18        7     shrub  0.00000000
## 757  2016 emergence          Tecopa  19        7     shrub  0.00000000
## 759  2016 emergence          Tecopa  20        7     shrub  0.00000000
## 761  2016 emergence          Tecopa  21        7     shrub  0.00000000
## 763  2016 emergence          Tecopa  22        7     shrub  0.00000000
## 765  2016 emergence          Tecopa  23        7     shrub  0.00000000
## 767  2016 emergence          Tecopa  24        7     shrub  0.00000000
## 769  2016 emergence          Tecopa  25        7     shrub  0.00000000
## 771  2016 emergence          Tecopa  26        7     shrub  0.00000000
## 773  2016 emergence          Tecopa  27        7     shrub  0.00000000
## 775  2016 emergence          Tecopa  28        7     shrub  0.00000000
## 777  2016 emergence          Tecopa  29        7     shrub -0.33333333
## 779  2016 emergence          Tecopa  30        7     shrub  0.00000000
## 781  2016 emergence      TejonRanch   1        3     shrub  0.00000000
## 783  2016 emergence      TejonRanch   2        3     shrub  0.00000000
## 785  2016 emergence      TejonRanch   3        3     shrub  0.00000000
## 787  2016 emergence      TejonRanch   4        3     shrub  0.00000000
## 789  2016 emergence      TejonRanch   5        3     shrub -1.00000000
## 791  2016 emergence      TejonRanch   6        3     shrub  0.00000000
## 793  2016 emergence      TejonRanch   7        3     shrub  0.00000000
## 795  2016 emergence      TejonRanch   8        3     shrub  0.00000000
## 797  2016 emergence      TejonRanch   9        3     shrub -0.50000000
## 799  2016 emergence      TejonRanch  10        3     shrub  0.00000000
## 801  2016 emergence      TejonRanch  11        3     shrub  0.00000000
## 803  2016 emergence      TejonRanch  12        3     shrub  0.00000000
## 805  2016 emergence      TejonRanch  13        3     shrub  0.00000000
## 807  2016 emergence      TejonRanch  14        3     shrub -1.00000000
## 809  2016 emergence      TejonRanch  15        3     shrub  0.00000000
## 811  2016 emergence      TejonRanch  16        3     shrub  0.00000000
## 813  2016 emergence      TejonRanch  17        3     shrub  1.00000000
## 815  2016 emergence      TejonRanch  18        3     shrub  0.00000000
## 817  2016 emergence      TejonRanch  19        3     shrub  1.00000000
## 819  2016 emergence      TejonRanch  20        3     shrub  0.00000000
## 821  2016 emergence      TejonRanch  21        3     shrub -1.00000000
## 823  2016 emergence      TejonRanch  22        3     shrub  0.00000000
## 825  2016 emergence      TejonRanch  23        3     shrub  1.00000000
## 827  2016 emergence      TejonRanch  24        3     shrub  1.00000000
## 829  2016 emergence      TejonRanch  25        3     shrub  1.00000000
## 831  2016 emergence      TejonRanch  26        3     shrub  0.00000000
## 833  2016 emergence      TejonRanch  27        3     shrub  1.00000000
## 835  2016 emergence      TejonRanch  28        3     shrub  0.00000000
## 837  2016 emergence      TejonRanch  29        3     shrub  0.00000000
## 839  2016 emergence      TejonRanch  30        3     shrub  0.00000000
## 841  2017 emergence    PanocheHills   1        1     shrub -0.14285714
## 843  2017 emergence    PanocheHills   2        1     shrub  0.00000000
## 845  2017 emergence    PanocheHills   3        1     shrub  0.33333333
## 847  2017 emergence    PanocheHills   4        1     shrub  1.00000000
## 849  2017 emergence    PanocheHills   5        1     shrub  1.00000000
## 851  2017 emergence    PanocheHills   6        1     shrub  0.50000000
## 853  2017 emergence    PanocheHills   7        1     shrub  1.00000000
## 855  2017 emergence    PanocheHills   8        1     shrub  0.71428571
## 857  2017 emergence    PanocheHills   9        1     shrub  0.71428571
## 859  2017 emergence    PanocheHills  10        1     shrub  0.60000000
## 861  2017 emergence    PanocheHills  11        1     shrub  0.00000000
## 863  2017 emergence    PanocheHills  12        1     shrub  1.00000000
## 865  2017 emergence    PanocheHills  13        1     shrub -0.60000000
## 867  2017 emergence    PanocheHills  14        1     shrub -0.14285714
## 869  2017 emergence    PanocheHills  15        1     shrub  0.00000000
## 871  2017 emergence    PanocheHills  16        1     shrub  1.00000000
## 873  2017 emergence    PanocheHills  17        1     shrub -1.00000000
## 875  2017 emergence    PanocheHills  18        1     shrub  1.00000000
## 877  2017 emergence    PanocheHills  19        1     shrub  0.00000000
## 879  2017 emergence    PanocheHills  20        1     shrub  1.00000000
## 881  2017 emergence    PanocheHills  21        1     shrub  1.00000000
## 883  2017 emergence    PanocheHills  22        1     shrub  0.00000000
## 885  2017 emergence    PanocheHills  23        1     shrub  1.00000000
## 887  2017 emergence    PanocheHills  24        1     shrub -0.75000000
## 889  2017 emergence    PanocheHills  25        1     shrub  1.00000000
## 891  2017 emergence    PanocheHills  26        1     shrub  0.00000000
## 893  2017 emergence    PanocheHills  27        1     shrub  1.00000000
## 895  2017 emergence    PanocheHills  28        1     shrub  1.00000000
## 897  2017 emergence    PanocheHills  29        1     shrub  1.00000000
## 899  2017 emergence    PanocheHills  30        1     shrub -0.33333333
## 901  2017 emergence          Cuyama   1        2     shrub  0.00000000
## 903  2017 emergence          Cuyama   2        2     shrub -1.00000000
## 905  2017 emergence          Cuyama   3        2     shrub -1.00000000
## 907  2017 emergence          Cuyama   4        2     shrub  1.00000000
## 909  2017 emergence          Cuyama   5        2     shrub  1.00000000
## 911  2017 emergence          Cuyama   6        2     shrub  1.00000000
## 913  2017 emergence          Cuyama   7        2     shrub -1.00000000
## 915  2017 emergence          Cuyama   8        2     shrub  0.00000000
## 917  2017 emergence          Cuyama   9        2     shrub  0.00000000
## 919  2017 emergence          Cuyama  10        2     shrub  0.33333333
## 921  2017 emergence          Cuyama  11        2     shrub  1.00000000
## 923  2017 emergence          Cuyama  12        2     shrub -1.00000000
## 925  2017 emergence          Cuyama  13        2     shrub -1.00000000
## 927  2017 emergence          Cuyama  14        2     shrub -1.00000000
## 929  2017 emergence          Cuyama  15        2     shrub  0.33333333
## 931  2017 emergence          Cuyama  16        2     shrub  1.00000000
## 933  2017 emergence          Cuyama  17        2     shrub  0.50000000
## 935  2017 emergence          Cuyama  18        2     shrub  0.00000000
## 937  2017 emergence          Cuyama  19        2     shrub -1.00000000
## 939  2017 emergence          Cuyama  20        2     shrub  1.00000000
## 941  2017 emergence          Cuyama  21        2     shrub  0.00000000
## 943  2017 emergence          Cuyama  22        2     shrub  1.00000000
## 945  2017 emergence          Cuyama  23        2     shrub  0.00000000
## 947  2017 emergence          Cuyama  24        2     shrub -1.00000000
## 949  2017 emergence          Cuyama  25        2     shrub -1.00000000
## 951  2017 emergence          Cuyama  26        2     shrub  1.00000000
## 953  2017 emergence          Cuyama  27        2     shrub -0.14285714
## 955  2017 emergence          Cuyama  28        2     shrub  0.00000000
## 957  2017 emergence          Cuyama  29        2     shrub  0.00000000
## 959  2017 emergence          Cuyama  30        2     shrub  0.00000000
## 961  2017 emergence         Barstow   1        4     shrub  1.00000000
## 963  2017 emergence         Barstow   2        4     shrub  1.00000000
## 965  2017 emergence         Barstow   3        4     shrub  1.00000000
## 967  2017 emergence         Barstow   4        4     shrub  0.00000000
## 969  2017 emergence         Barstow   5        4     shrub -0.33333333
## 971  2017 emergence         Barstow   6        4     shrub  0.60000000
## 973  2017 emergence         Barstow   7        4     shrub  1.00000000
## 975  2017 emergence         Barstow   8        4     shrub  1.00000000
## 977  2017 emergence         Barstow   9        4     shrub  0.00000000
## 979  2017 emergence         Barstow  10        4     shrub  1.00000000
## 981  2017 emergence         Barstow  11        4     shrub  0.00000000
## 983  2017 emergence         Barstow  12        4     shrub  0.00000000
## 985  2017 emergence         Barstow  13        4     shrub  1.00000000
## 987  2017 emergence         Barstow  14        4     shrub  0.00000000
## 989  2017 emergence         Barstow  15        4     shrub  0.00000000
## 991  2017 emergence         Barstow  16        4     shrub  1.00000000
## 993  2017 emergence         Barstow  17        4     shrub  1.00000000
## 995  2017 emergence         Barstow  18        4     shrub  0.00000000
## 997  2017 emergence         Barstow  19        4     shrub -0.33333333
## 999  2017 emergence         Barstow  20        4     shrub  0.00000000
## 1001 2017 emergence         Barstow  21        4     shrub  0.00000000
## 1003 2017 emergence         Barstow  22        4     shrub  0.00000000
## 1005 2017 emergence         Barstow  23        4     shrub  1.00000000
## 1007 2017 emergence         Barstow  24        4     shrub  1.00000000
## 1009 2017 emergence         Barstow  25        4     shrub  0.50000000
## 1011 2017 emergence         Barstow  26        4     shrub  0.25000000
## 1013 2017 emergence         Barstow  27        4     shrub  1.00000000
## 1015 2017 emergence         Barstow  28        4     shrub  0.23076923
## 1017 2017 emergence         Barstow  29        4     shrub -1.00000000
## 1019 2017 emergence         Barstow  30        4     shrub  1.00000000
## 1021 2017 emergence   HeartofMojave   1        5     shrub -0.33333333
## 1023 2017 emergence   HeartofMojave   2        5     shrub  0.00000000
## 1025 2017 emergence   HeartofMojave   3        5     shrub  1.00000000
## 1027 2017 emergence   HeartofMojave   4        5     shrub  0.00000000
## 1029 2017 emergence   HeartofMojave   5        5     shrub  1.00000000
## 1031 2017 emergence   HeartofMojave   6        5     shrub  0.00000000
## 1033 2017 emergence   HeartofMojave   7        5     shrub  0.00000000
## 1035 2017 emergence   HeartofMojave   8        5     shrub -1.00000000
## 1037 2017 emergence   HeartofMojave   9        5     shrub  0.00000000
## 1039 2017 emergence   HeartofMojave  10        5     shrub  1.00000000
## 1041 2017 emergence   HeartofMojave  11        5     shrub  1.00000000
## 1043 2017 emergence   HeartofMojave  12        5     shrub  0.00000000
## 1045 2017 emergence   HeartofMojave  13        5     shrub  0.00000000
## 1047 2017 emergence   HeartofMojave  14        5     shrub  0.00000000
## 1049 2017 emergence   HeartofMojave  15        5     shrub -1.00000000
## 1051 2017 emergence   HeartofMojave  16        5     shrub -1.00000000
## 1053 2017 emergence   HeartofMojave  17        5     shrub -1.00000000
## 1055 2017 emergence   HeartofMojave  18        5     shrub -1.00000000
## 1057 2017 emergence   HeartofMojave  19        5     shrub  0.25000000
## 1059 2017 emergence   HeartofMojave  20        5     shrub -0.12500000
## 1061 2017 emergence   HeartofMojave  21        5     shrub -1.00000000
## 1063 2017 emergence   HeartofMojave  22        5     shrub  0.33333333
## 1065 2017 emergence   HeartofMojave  23        5     shrub  1.00000000
## 1067 2017 emergence   HeartofMojave  24        5     shrub  0.00000000
## 1069 2017 emergence   HeartofMojave  25        5     shrub  0.60000000
## 1071 2017 emergence   HeartofMojave  26        5     shrub  0.00000000
## 1073 2017 emergence   HeartofMojave  27        5     shrub  0.00000000
## 1075 2017 emergence   HeartofMojave  28        5     shrub  1.00000000
## 1077 2017 emergence   HeartofMojave  29        5     shrub  0.00000000
## 1079 2017 emergence   HeartofMojave  30        5     shrub  0.00000000
## 1081 2017 emergence SheepholeValley   1        6     shrub  1.00000000
## 1083 2017 emergence SheepholeValley   2        6     shrub  0.33333333
## 1085 2017 emergence SheepholeValley   3        6     shrub  0.50000000
## 1087 2017 emergence SheepholeValley   4        6     shrub  0.42857143
## 1089 2017 emergence SheepholeValley   5        6     shrub  1.00000000
## 1091 2017 emergence SheepholeValley   6        6     shrub  0.00000000
## 1093 2017 emergence SheepholeValley   7        6     shrub  1.00000000
## 1095 2017 emergence SheepholeValley   8        6     shrub  1.00000000
## 1097 2017 emergence SheepholeValley   9        6     shrub  1.00000000
## 1099 2017 emergence SheepholeValley  10        6     shrub  0.00000000
## 1101 2017 emergence SheepholeValley  11        6     shrub -0.33333333
## 1103 2017 emergence SheepholeValley  12        6     shrub  1.00000000
## 1105 2017 emergence SheepholeValley  13        6     shrub -0.33333333
## 1107 2017 emergence SheepholeValley  14        6     shrub  0.00000000
## 1109 2017 emergence SheepholeValley  15        6     shrub  1.00000000
## 1111 2017 emergence SheepholeValley  16        6     shrub  0.42857143
## 1113 2017 emergence SheepholeValley  17        6     shrub  0.00000000
## 1115 2017 emergence SheepholeValley  18        6     shrub  0.00000000
## 1117 2017 emergence SheepholeValley  19        6     shrub  1.00000000
## 1119 2017 emergence SheepholeValley  20        6     shrub  0.50000000
## 1121 2017 emergence SheepholeValley  21        6     shrub  1.00000000
## 1123 2017 emergence SheepholeValley  22        6     shrub  0.63636364
## 1125 2017 emergence SheepholeValley  23        6     shrub  1.00000000
## 1127 2017 emergence SheepholeValley  24        6     shrub -1.00000000
## 1129 2017 emergence SheepholeValley  25        6     shrub  0.33333333
## 1131 2017 emergence SheepholeValley  26        6     shrub -0.45454545
## 1133 2017 emergence SheepholeValley  27        6     shrub -0.33333333
## 1135 2017 emergence SheepholeValley  28        6     shrub  1.00000000
## 1137 2017 emergence SheepholeValley  29        6     shrub  1.00000000
## 1139 2017 emergence SheepholeValley  30        6     shrub  0.00000000
## 1141 2017 emergence          Tecopa   1        7     shrub -1.00000000
## 1143 2017 emergence          Tecopa   2        7     shrub  1.00000000
## 1145 2017 emergence          Tecopa   3        7     shrub  1.00000000
## 1147 2017 emergence          Tecopa   4        7     shrub  0.00000000
## 1149 2017 emergence          Tecopa   5        7     shrub  0.00000000
## 1151 2017 emergence          Tecopa   6        7     shrub  0.00000000
## 1153 2017 emergence          Tecopa   7        7     shrub  0.00000000
## 1155 2017 emergence          Tecopa   8        7     shrub  0.00000000
## 1157 2017 emergence          Tecopa   9        7     shrub -1.00000000
## 1159 2017 emergence          Tecopa  10        7     shrub  0.00000000
## 1161 2017 emergence          Tecopa  11        7     shrub  1.00000000
## 1163 2017 emergence          Tecopa  12        7     shrub -1.00000000
## 1165 2017 emergence          Tecopa  13        7     shrub -1.00000000
## 1167 2017 emergence          Tecopa  14        7     shrub -1.00000000
## 1169 2017 emergence          Tecopa  15        7     shrub -1.00000000
## 1171 2017 emergence          Tecopa  16        7     shrub  1.00000000
## 1173 2017 emergence          Tecopa  17        7     shrub  1.00000000
## 1175 2017 emergence          Tecopa  18        7     shrub  0.00000000
## 1177 2017 emergence          Tecopa  19        7     shrub  1.00000000
## 1179 2017 emergence          Tecopa  20        7     shrub -1.00000000
## 1181 2017 emergence          Tecopa  21        7     shrub  0.00000000
## 1183 2017 emergence          Tecopa  22        7     shrub  1.00000000
## 1185 2017 emergence          Tecopa  23        7     shrub  1.00000000
## 1187 2017 emergence          Tecopa  24        7     shrub  0.00000000
## 1189 2017 emergence          Tecopa  25        7     shrub  0.00000000
## 1191 2017 emergence          Tecopa  26        7     shrub -1.00000000
## 1193 2017 emergence          Tecopa  27        7     shrub  0.50000000
## 1195 2017 emergence          Tecopa  28        7     shrub  0.00000000
## 1197 2017 emergence          Tecopa  29        7     shrub  1.00000000
## 1199 2017 emergence          Tecopa  30        7     shrub -1.00000000
## 1201 2017 emergence      TejonRanch   1        3     shrub  0.00000000
## 1203 2017 emergence      TejonRanch   2        3     shrub  1.00000000
## 1205 2017 emergence      TejonRanch   3        3     shrub  0.00000000
## 1207 2017 emergence      TejonRanch   4        3     shrub  1.00000000
## 1209 2017 emergence      TejonRanch   5        3     shrub  0.00000000
## 1211 2017 emergence      TejonRanch   6        3     shrub  0.00000000
## 1213 2017 emergence      TejonRanch   7        3     shrub  0.00000000
## 1215 2017 emergence      TejonRanch   8        3     shrub  0.00000000
## 1217 2017 emergence      TejonRanch   9        3     shrub  1.00000000
## 1219 2017 emergence      TejonRanch  10        3     shrub  0.00000000
## 1221 2017 emergence      TejonRanch  11        3     shrub  0.00000000
## 1223 2017 emergence      TejonRanch  12        3     shrub  0.00000000
## 1225 2017 emergence      TejonRanch  13        3     shrub  0.00000000
## 1227 2017 emergence      TejonRanch  14        3     shrub  0.00000000
## 1229 2017 emergence      TejonRanch  15        3     shrub  0.00000000
## 1231 2017 emergence      TejonRanch  16        3     shrub  0.00000000
## 1233 2017 emergence      TejonRanch  17        3     shrub  1.00000000
## 1235 2017 emergence      TejonRanch  18        3     shrub  1.00000000
## 1237 2017 emergence      TejonRanch  19        3     shrub -1.00000000
## 1239 2017 emergence      TejonRanch  20        3     shrub  0.00000000
## 1241 2017 emergence      TejonRanch  21        3     shrub  0.33333333
## 1243 2017 emergence      TejonRanch  22        3     shrub  0.00000000
## 1245 2017 emergence      TejonRanch  23        3     shrub  0.00000000
## 1247 2017 emergence      TejonRanch  24        3     shrub  0.00000000
## 1249 2017 emergence      TejonRanch  25        3     shrub  0.00000000
## 1251 2017 emergence      TejonRanch  26        3     shrub  0.00000000
## 1253 2017 emergence      TejonRanch  27        3     shrub  0.00000000
## 1255 2017 emergence      TejonRanch  28        3     shrub  0.00000000
## 1257 2017 emergence      TejonRanch  29        3     shrub  1.00000000
## 1259 2017 emergence      TejonRanch  30        3     shrub  1.00000000
## 1261 2017       end    PanocheHills   1        1     shrub  0.00000000
## 1263 2017       end    PanocheHills   2        1     shrub  0.00000000
## 1265 2017       end    PanocheHills   3        1     shrub  0.00000000
## 1267 2017       end    PanocheHills   4        1     shrub  0.00000000
## 1269 2017       end    PanocheHills   5        1     shrub  0.00000000
## 1271 2017       end    PanocheHills   6        1     shrub  0.00000000
## 1273 2017       end    PanocheHills   7        1     shrub  0.00000000
## 1275 2017       end    PanocheHills   8        1     shrub  0.00000000
## 1277 2017       end    PanocheHills   9        1     shrub  0.00000000
## 1279 2017       end    PanocheHills  10        1     shrub  0.00000000
## 1281 2017       end    PanocheHills  11        1     shrub  0.00000000
## 1283 2017       end    PanocheHills  12        1     shrub  0.00000000
## 1285 2017       end    PanocheHills  13        1     shrub  0.00000000
## 1287 2017       end    PanocheHills  14        1     shrub  0.00000000
## 1289 2017       end    PanocheHills  15        1     shrub  0.00000000
## 1291 2017       end    PanocheHills  16        1     shrub  0.00000000
## 1293 2017       end    PanocheHills  17        1     shrub  0.00000000
## 1295 2017       end    PanocheHills  18        1     shrub  0.00000000
## 1297 2017       end    PanocheHills  19        1     shrub  0.00000000
## 1299 2017       end    PanocheHills  20        1     shrub  0.00000000
## 1301 2017       end    PanocheHills  21        1     shrub  0.00000000
## 1303 2017       end    PanocheHills  22        1     shrub  0.00000000
## 1305 2017       end    PanocheHills  23        1     shrub  0.00000000
## 1307 2017       end    PanocheHills  24        1     shrub  0.00000000
## 1309 2017       end    PanocheHills  25        1     shrub  0.00000000
## 1311 2017       end    PanocheHills  26        1     shrub  0.00000000
## 1313 2017       end    PanocheHills  27        1     shrub  0.00000000
## 1315 2017       end    PanocheHills  28        1     shrub  0.00000000
## 1317 2017       end    PanocheHills  29        1     shrub  0.00000000
## 1319 2017       end    PanocheHills  30        1     shrub  0.00000000
## 1321 2017       end          Cuyama   1        2     shrub  0.00000000
## 1323 2017       end          Cuyama   2        2     shrub  0.00000000
## 1325 2017       end          Cuyama   3        2     shrub  0.00000000
## 1327 2017       end          Cuyama   4        2     shrub  0.00000000
## 1329 2017       end          Cuyama   5        2     shrub  0.00000000
## 1331 2017       end          Cuyama   6        2     shrub  1.00000000
## 1333 2017       end          Cuyama   7        2     shrub  0.00000000
## 1335 2017       end          Cuyama   8        2     shrub  0.00000000
## 1337 2017       end          Cuyama   9        2     shrub  1.00000000
## 1339 2017       end          Cuyama  10        2     shrub  1.00000000
## 1341 2017       end          Cuyama  11        2     shrub  1.00000000
## 1343 2017       end          Cuyama  12        2     shrub  0.00000000
## 1345 2017       end          Cuyama  13        2     shrub  0.00000000
## 1347 2017       end          Cuyama  14        2     shrub  1.00000000
## 1349 2017       end          Cuyama  15        2     shrub  0.00000000
## 1351 2017       end          Cuyama  16        2     shrub  1.00000000
## 1353 2017       end          Cuyama  17        2     shrub  1.00000000
## 1355 2017       end          Cuyama  18        2     shrub  0.00000000
## 1357 2017       end          Cuyama  19        2     shrub  1.00000000
## 1359 2017       end          Cuyama  20        2     shrub  1.00000000
## 1361 2017       end          Cuyama  21        2     shrub  0.00000000
## 1363 2017       end          Cuyama  22        2     shrub  0.00000000
## 1365 2017       end          Cuyama  23        2     shrub  1.00000000
## 1367 2017       end          Cuyama  24        2     shrub  1.00000000
## 1369 2017       end          Cuyama  25        2     shrub  0.00000000
## 1371 2017       end          Cuyama  26        2     shrub  0.00000000
## 1373 2017       end          Cuyama  27        2     shrub  0.00000000
## 1375 2017       end          Cuyama  28        2     shrub  0.00000000
## 1377 2017       end          Cuyama  29        2     shrub  0.00000000
## 1379 2017       end          Cuyama  30        2     shrub  0.00000000
## 1381 2017       end         Barstow   1        4     shrub  1.00000000
## 1383 2017       end         Barstow   2        4     shrub  1.00000000
## 1385 2017       end         Barstow   3        4     shrub  0.00000000
## 1387 2017       end         Barstow   4        4     shrub  0.00000000
## 1389 2017       end         Barstow   5        4     shrub  1.00000000
## 1391 2017       end         Barstow   6        4     shrub  0.66666667
## 1393 2017       end         Barstow   7        4     shrub  1.00000000
## 1395 2017       end         Barstow   8        4     shrub -1.00000000
## 1397 2017       end         Barstow   9        4     shrub  0.00000000
## 1399 2017       end         Barstow  10        4     shrub  0.00000000
## 1401 2017       end         Barstow  11        4     shrub -1.00000000
## 1403 2017       end         Barstow  12        4     shrub  0.00000000
## 1405 2017       end         Barstow  13        4     shrub -1.00000000
## 1407 2017       end         Barstow  14        4     shrub  0.00000000
## 1409 2017       end         Barstow  15        4     shrub  0.00000000
## 1411 2017       end         Barstow  16        4     shrub  0.00000000
## 1413 2017       end         Barstow  17        4     shrub  1.00000000
## 1415 2017       end         Barstow  18        4     shrub  0.00000000
## 1417 2017       end         Barstow  19        4     shrub  0.00000000
## 1419 2017       end         Barstow  20        4     shrub  0.00000000
## 1421 2017       end         Barstow  21        4     shrub  0.00000000
## 1423 2017       end         Barstow  22        4     shrub  0.33333333
## 1425 2017       end         Barstow  23        4     shrub  1.00000000
## 1427 2017       end         Barstow  24        4     shrub  1.00000000
## 1429 2017       end         Barstow  25        4     shrub  0.50000000
## 1431 2017       end         Barstow  26        4     shrub  0.00000000
## 1433 2017       end         Barstow  27        4     shrub  0.00000000
## 1435 2017       end         Barstow  28        4     shrub  1.00000000
## 1437 2017       end         Barstow  29        4     shrub -1.00000000
## 1439 2017       end         Barstow  30        4     shrub  0.00000000
## 1441 2017       end   HeartofMojave   1        5     shrub  0.33333333
## 1443 2017       end   HeartofMojave   2        5     shrub  1.00000000
## 1445 2017       end   HeartofMojave   3        5     shrub  0.00000000
## 1447 2017       end   HeartofMojave   4        5     shrub  1.00000000
## 1449 2017       end   HeartofMojave   5        5     shrub  1.00000000
## 1451 2017       end   HeartofMojave   6        5     shrub  0.00000000
## 1453 2017       end   HeartofMojave   7        5     shrub  0.00000000
## 1455 2017       end   HeartofMojave   8        5     shrub -1.00000000
## 1457 2017       end   HeartofMojave   9        5     shrub  1.00000000
## 1459 2017       end   HeartofMojave  10        5     shrub  1.00000000
## 1461 2017       end   HeartofMojave  11        5     shrub  1.00000000
## 1463 2017       end   HeartofMojave  12        5     shrub  1.00000000
## 1465 2017       end   HeartofMojave  13        5     shrub  1.00000000
## 1467 2017       end   HeartofMojave  14        5     shrub  1.00000000
## 1469 2017       end   HeartofMojave  15        5     shrub -1.00000000
## 1471 2017       end   HeartofMojave  16        5     shrub  0.00000000
## 1473 2017       end   HeartofMojave  17        5     shrub -1.00000000
## 1475 2017       end   HeartofMojave  18        5     shrub -1.00000000
## 1477 2017       end   HeartofMojave  19        5     shrub  1.00000000
## 1479 2017       end   HeartofMojave  20        5     shrub  0.09090909
## 1481 2017       end   HeartofMojave  21        5     shrub -0.50000000
## 1483 2017       end   HeartofMojave  22        5     shrub  0.50000000
## 1485 2017       end   HeartofMojave  23        5     shrub  1.00000000
## 1487 2017       end   HeartofMojave  24        5     shrub  1.00000000
## 1489 2017       end   HeartofMojave  25        5     shrub -0.20000000
## 1491 2017       end   HeartofMojave  26        5     shrub  1.00000000
## 1493 2017       end   HeartofMojave  27        5     shrub  1.00000000
## 1495 2017       end   HeartofMojave  28        5     shrub  1.00000000
## 1497 2017       end   HeartofMojave  29        5     shrub  0.00000000
## 1499 2017       end   HeartofMojave  30        5     shrub  1.00000000
## 1501 2017       end SheepholeValley   1        6     shrub  1.00000000
## 1503 2017       end SheepholeValley   2        6     shrub  1.00000000
## 1505 2017       end SheepholeValley   3        6     shrub  0.50000000
## 1507 2017       end SheepholeValley   4        6     shrub  0.71428571
## 1509 2017       end SheepholeValley   5        6     shrub  0.00000000
## 1511 2017       end SheepholeValley   6        6     shrub  1.00000000
## 1513 2017       end SheepholeValley   7        6     shrub  1.00000000
## 1515 2017       end SheepholeValley   8        6     shrub  0.00000000
## 1517 2017       end SheepholeValley   9        6     shrub  1.00000000
## 1519 2017       end SheepholeValley  10        6     shrub  1.00000000
## 1521 2017       end SheepholeValley  11        6     shrub  0.00000000
## 1523 2017       end SheepholeValley  12        6     shrub  0.45454545
## 1525 2017       end SheepholeValley  13        6     shrub  1.00000000
## 1527 2017       end SheepholeValley  14        6     shrub  0.00000000
## 1529 2017       end SheepholeValley  15        6     shrub  0.00000000
## 1531 2017       end SheepholeValley  16        6     shrub  0.50000000
## 1533 2017       end SheepholeValley  17        6     shrub  1.00000000
## 1535 2017       end SheepholeValley  18        6     shrub -1.00000000
## 1537 2017       end SheepholeValley  19        6     shrub  1.00000000
## 1539 2017       end SheepholeValley  20        6     shrub  1.00000000
## 1541 2017       end SheepholeValley  21        6     shrub  1.00000000
## 1543 2017       end SheepholeValley  22        6     shrub  0.42857143
## 1545 2017       end SheepholeValley  23        6     shrub  1.00000000
## 1547 2017       end SheepholeValley  24        6     shrub -1.00000000
## 1549 2017       end SheepholeValley  25        6     shrub  0.00000000
## 1551 2017       end SheepholeValley  26        6     shrub -0.33333333
## 1553 2017       end SheepholeValley  27        6     shrub  0.25000000
## 1555 2017       end SheepholeValley  28        6     shrub  1.00000000
## 1557 2017       end SheepholeValley  29        6     shrub  1.00000000
## 1559 2017       end SheepholeValley  30        6     shrub -1.00000000
## 1561 2017       end          Tecopa   1        7     shrub  0.00000000
## 1563 2017       end          Tecopa   2        7     shrub  0.00000000
## 1565 2017       end          Tecopa   3        7     shrub  0.00000000
## 1567 2017       end          Tecopa   4        7     shrub  0.00000000
## 1569 2017       end          Tecopa   5        7     shrub  0.00000000
## 1571 2017       end          Tecopa   6        7     shrub  0.00000000
## 1573 2017       end          Tecopa   7        7     shrub  0.00000000
## 1575 2017       end          Tecopa   8        7     shrub  0.00000000
## 1577 2017       end          Tecopa   9        7     shrub  0.00000000
## 1579 2017       end          Tecopa  10        7     shrub  0.00000000
## 1581 2017       end          Tecopa  11        7     shrub  1.00000000
## 1583 2017       end          Tecopa  12        7     shrub  0.00000000
## 1585 2017       end          Tecopa  13        7     shrub -1.00000000
## 1587 2017       end          Tecopa  14        7     shrub  0.00000000
## 1589 2017       end          Tecopa  15        7     shrub -1.00000000
## 1591 2017       end          Tecopa  16        7     shrub  1.00000000
## 1593 2017       end          Tecopa  17        7     shrub  1.00000000
## 1595 2017       end          Tecopa  18        7     shrub  0.00000000
## 1597 2017       end          Tecopa  19        7     shrub  0.00000000
## 1599 2017       end          Tecopa  20        7     shrub  0.00000000
## 1601 2017       end          Tecopa  21        7     shrub  0.00000000
## 1603 2017       end          Tecopa  22        7     shrub  0.00000000
## 1605 2017       end          Tecopa  23        7     shrub  0.00000000
## 1607 2017       end          Tecopa  24        7     shrub  0.00000000
## 1609 2017       end          Tecopa  25        7     shrub  0.00000000
## 1611 2017       end          Tecopa  26        7     shrub  0.00000000
## 1613 2017       end          Tecopa  27        7     shrub  0.00000000
## 1615 2017       end          Tecopa  28        7     shrub  0.00000000
## 1617 2017       end          Tecopa  29        7     shrub  0.00000000
## 1619 2017       end          Tecopa  30        7     shrub  0.00000000
## 1621 2017       end      TejonRanch   1        3     shrub  0.00000000
## 1623 2017       end      TejonRanch   2        3     shrub  0.00000000
## 1625 2017       end      TejonRanch   3        3     shrub  0.00000000
## 1627 2017       end      TejonRanch   4        3     shrub  0.00000000
## 1629 2017       end      TejonRanch   5        3     shrub  0.00000000
## 1631 2017       end      TejonRanch   6        3     shrub  0.00000000
## 1633 2017       end      TejonRanch   7        3     shrub  0.00000000
## 1635 2017       end      TejonRanch   8        3     shrub  0.00000000
## 1637 2017       end      TejonRanch   9        3     shrub  0.00000000
## 1639 2017       end      TejonRanch  10        3     shrub  0.00000000
## 1641 2017       end      TejonRanch  11        3     shrub  0.00000000
## 1643 2017       end      TejonRanch  12        3     shrub  0.00000000
## 1645 2017       end      TejonRanch  13        3     shrub  0.00000000
## 1647 2017       end      TejonRanch  14        3     shrub  0.00000000
## 1649 2017       end      TejonRanch  15        3     shrub  0.00000000
## 1651 2017       end      TejonRanch  16        3     shrub  0.00000000
## 1653 2017       end      TejonRanch  17        3     shrub  0.00000000
## 1655 2017       end      TejonRanch  18        3     shrub  0.00000000
## 1657 2017       end      TejonRanch  19        3     shrub  0.00000000
## 1659 2017       end      TejonRanch  20        3     shrub  0.00000000
## 1661 2017       end      TejonRanch  21        3     shrub  0.00000000
## 1663 2017       end      TejonRanch  22        3     shrub  0.00000000
## 1665 2017       end      TejonRanch  23        3     shrub  0.00000000
## 1667 2017       end      TejonRanch  24        3     shrub  0.00000000
## 1669 2017       end      TejonRanch  25        3     shrub  0.00000000
## 1671 2017       end      TejonRanch  26        3     shrub  0.00000000
## 1673 2017       end      TejonRanch  27        3     shrub  0.00000000
## 1675 2017       end      TejonRanch  28        3     shrub  0.00000000
## 1677 2017       end      TejonRanch  29        3     shrub  0.00000000
## 1679 2017       end      TejonRanch  30        3     shrub  0.00000000
##         Plantago      Salvia Phacelia.biomass Plantago.biomass
## 1    -1.00000000 -1.00000000       0.76911380        0.0000000
## 3    -1.00000000 -0.47368421       0.00000000        0.0000000
## 5     0.00000000  1.00000000       0.32484802        0.0000000
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## 15    0.14285714  0.20000000       0.83505590       -0.2862030
## 17   -1.00000000  0.00000000       0.77649955        0.0000000
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## 21   -1.00000000  0.16666667       0.00000000        0.0000000
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## 25    0.00000000  0.00000000       0.06547857        0.0000000
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## 29   -1.00000000 -0.25000000       0.89554296        0.0000000
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## 1239  0.00000000  1.00000000       0.00000000        0.0000000
## 1241  1.00000000 -1.00000000       0.00000000        0.0000000
## 1243  0.00000000  0.00000000       0.00000000        0.0000000
## 1245  0.00000000  0.00000000       0.00000000        0.0000000
## 1247 -1.00000000 -0.33333333       0.00000000        0.0000000
## 1249  0.00000000 -0.33333333       0.00000000        0.0000000
## 1251  0.00000000  1.00000000       0.00000000        0.0000000
## 1253  0.00000000  0.00000000       0.00000000        0.0000000
## 1255  0.20000000 -0.81818182       0.00000000        0.0000000
## 1257  0.00000000  0.20000000       0.00000000        0.0000000
## 1259  0.00000000 -0.71428571       0.00000000        0.0000000
## 1261  0.00000000  0.00000000       0.00000000        0.0000000
## 1263  0.00000000  0.00000000       0.00000000        0.0000000
## 1265  0.00000000 -1.00000000       0.00000000        0.0000000
## 1267  0.00000000 -1.00000000       0.00000000        0.0000000
## 1269  0.00000000 -1.00000000       0.00000000        0.0000000
## 1271  0.00000000  0.00000000       0.00000000        0.0000000
## 1273  0.00000000  0.00000000       0.00000000        0.0000000
## 1275  0.00000000  0.00000000       0.00000000        0.0000000
## 1277  0.00000000  0.00000000       0.00000000        0.0000000
## 1279  0.00000000 -0.78947368       0.00000000        0.0000000
## 1281  0.00000000  0.00000000       0.00000000        0.0000000
## 1283  0.00000000  0.00000000       0.00000000        0.0000000
## 1285  0.00000000  0.00000000       0.00000000        0.0000000
## 1287  0.00000000  0.00000000       0.00000000        0.0000000
## 1289  0.00000000  0.00000000       0.00000000        0.0000000
## 1291  0.00000000  0.00000000       0.00000000        0.0000000
## 1293  0.00000000  0.00000000       0.00000000        0.0000000
## 1295  0.00000000 -1.00000000       0.00000000        0.0000000
## 1297  0.00000000  0.00000000       0.00000000        0.0000000
## 1299  0.00000000  0.00000000       0.00000000        0.0000000
## 1301  0.00000000 -1.00000000       0.00000000        0.0000000
## 1303  0.00000000  0.00000000       0.00000000        0.0000000
## 1305  0.00000000 -0.09090909       0.00000000        0.0000000
## 1307  0.00000000  0.00000000       0.00000000        0.0000000
## 1309  0.00000000 -1.00000000       0.00000000        0.0000000
## 1311  0.00000000  0.00000000       0.00000000        0.0000000
## 1313  0.00000000 -1.00000000       0.00000000        0.0000000
## 1315  0.00000000  0.00000000       0.00000000        0.0000000
## 1317  0.00000000  0.00000000       0.00000000        0.0000000
## 1319  0.00000000  0.00000000       0.00000000        0.0000000
## 1321  0.00000000  0.00000000       0.00000000        0.0000000
## 1323  0.00000000  0.00000000       0.00000000        0.0000000
## 1325  0.00000000  0.00000000       0.00000000        0.0000000
## 1327  0.00000000  0.00000000       0.00000000        0.0000000
## 1329  0.00000000  0.00000000       0.00000000        0.0000000
## 1331  0.00000000  0.00000000       0.00000000        0.0000000
## 1333  0.00000000  0.00000000       0.00000000        0.0000000
## 1335  0.00000000  1.00000000       0.00000000        0.0000000
## 1337  0.00000000 -1.00000000       0.00000000        0.0000000
## 1339  0.00000000  0.00000000       0.00000000        0.0000000
## 1341  0.00000000  1.00000000       0.00000000        0.0000000
## 1343  0.00000000  0.00000000       0.00000000        0.0000000
## 1345  0.00000000 -1.00000000       0.00000000        0.0000000
## 1347  0.00000000  0.00000000       0.00000000        0.0000000
## 1349  0.00000000  0.00000000       0.00000000        0.0000000
## 1351  0.00000000  0.00000000       0.00000000        0.0000000
## 1353  0.00000000  0.00000000       0.00000000        0.0000000
## 1355  0.00000000  0.00000000       0.00000000        0.0000000
## 1357  0.00000000  0.00000000       0.00000000        0.0000000
## 1359  0.00000000  0.00000000       0.00000000        0.0000000
## 1361  0.00000000 -1.00000000       0.00000000        0.0000000
## 1363  0.00000000 -1.00000000       0.00000000        0.0000000
## 1365  0.00000000  0.20000000       0.00000000        0.0000000
## 1367  0.00000000  0.20000000       0.00000000        0.0000000
## 1369  0.00000000  0.00000000       0.00000000        0.0000000
## 1371  0.00000000  0.00000000       0.00000000        0.0000000
## 1373  0.00000000  0.00000000       0.00000000        0.0000000
## 1375  0.00000000  0.00000000       0.00000000        0.0000000
## 1377  0.00000000  0.00000000       0.00000000        0.0000000
## 1379  0.00000000  0.00000000       0.00000000        0.0000000
## 1381  0.33333333  0.00000000       0.00000000        0.3183256
## 1383  0.00000000  1.00000000       0.00000000        0.0000000
## 1385  0.00000000  0.00000000       0.00000000        0.0000000
## 1387  0.00000000  0.00000000       0.00000000        0.0000000
## 1389  0.00000000  0.00000000       0.00000000        0.0000000
## 1391  1.00000000 -1.00000000       0.00000000        0.0000000
## 1393  0.33333333  0.00000000       0.00000000        0.0000000
## 1395 -1.00000000 -1.00000000       0.00000000        0.0000000
## 1397  0.00000000 -1.00000000       0.00000000        0.0000000
## 1399  0.00000000 -1.00000000       0.00000000        0.0000000
## 1401  0.00000000 -1.00000000       0.00000000        0.0000000
## 1403  0.00000000  1.00000000       0.00000000        0.0000000
## 1405  0.75000000 -1.00000000       0.00000000        0.0000000
## 1407  0.00000000  0.00000000       0.00000000        0.0000000
## 1409 -1.00000000  0.00000000       0.00000000        0.0000000
## 1411 -0.20000000  0.00000000       0.00000000        0.1007026
## 1413  0.00000000  0.00000000       0.00000000        0.0000000
## 1415  0.00000000  0.00000000       0.00000000        0.0000000
## 1417 -1.00000000  1.00000000       0.00000000        0.4674115
## 1419  0.00000000  1.00000000       0.00000000        0.0000000
## 1421 -1.00000000 -1.00000000       0.00000000        0.0000000
## 1423 -1.00000000  1.00000000       0.86429725        0.0000000
## 1425  0.20000000 -1.00000000       0.00000000        0.0000000
## 1427  0.42857143 -1.00000000       0.00000000        0.0000000
## 1429  0.16666667  0.00000000       0.00000000        0.0000000
## 1431  0.55555556  0.00000000       0.00000000        0.0000000
## 1433  1.00000000  1.00000000       0.00000000        0.0000000
## 1435 -1.00000000 -1.00000000       0.00000000        0.0000000
## 1437 -1.00000000 -1.00000000       0.00000000        0.0000000
## 1439  0.00000000  0.00000000       0.00000000        0.0000000
## 1441 -1.00000000  0.60000000       0.76596173        0.0000000
## 1443  0.00000000  1.00000000       0.00000000        0.0000000
## 1445  0.00000000  0.00000000       0.00000000        0.0000000
## 1447  0.00000000 -0.33333333       0.00000000        0.0000000
## 1449 -1.00000000  1.00000000       0.00000000        0.0000000
## 1451  0.55555556  0.80000000       0.00000000        0.0000000
## 1453 -1.00000000 -0.60000000       0.00000000        0.0000000
## 1455  0.00000000 -1.00000000       0.00000000        0.0000000
## 1457  0.00000000  0.22222222       0.00000000        0.0000000
## 1459  0.00000000  1.00000000       0.00000000        0.0000000
## 1461  0.00000000 -0.66666667       0.00000000        0.0000000
## 1463  0.00000000  0.14285714       0.00000000        0.0000000
## 1465  0.00000000 -0.20000000       0.00000000        0.0000000
## 1467 -1.00000000  0.00000000       0.00000000        0.0000000
## 1469 -1.00000000 -0.55555556       0.00000000        0.0000000
## 1471  1.00000000 -0.25000000       0.00000000        0.0000000
## 1473  0.00000000 -1.00000000       0.00000000       -0.3837589
## 1475  0.00000000  0.60000000       0.00000000        0.0000000
## 1477 -1.00000000 -0.50000000       0.00000000        0.0000000
## 1479  1.00000000  0.04761905      -0.74688628        0.0000000
## 1481 -0.69230769 -1.00000000      -0.64871155       -0.8664591
## 1483  0.00000000 -0.14285714       0.34375906        0.0000000
## 1485 -0.25000000 -0.52941176       0.00000000       -0.6407306
## 1487  0.00000000  0.23809524       0.00000000        0.0000000
## 1489 -1.00000000 -0.57142857       0.20012399        0.0000000
## 1491  0.00000000  1.00000000       0.00000000        0.0000000
## 1493 -1.00000000 -0.33333333       0.00000000        0.0000000
## 1495  1.00000000  1.00000000       0.00000000        0.0000000
## 1497 -1.00000000 -1.00000000       0.00000000        0.0000000
## 1499  0.00000000  1.00000000       0.00000000        0.0000000
## 1501  0.42857143 -1.00000000       0.00000000        0.6107134
## 1503  1.00000000  0.38461538       0.00000000        0.0000000
## 1505  1.00000000  0.33333333       0.59398587        0.0000000
## 1507 -1.00000000 -0.33333333       0.89382424        0.0000000
## 1509 -0.33333333 -1.00000000       0.00000000       -0.7835467
## 1511 -1.00000000  0.00000000       0.00000000        0.0000000
## 1513  0.00000000  1.00000000       0.00000000        0.0000000
## 1515 -1.00000000 -0.50000000       0.00000000        0.0000000
## 1517  1.00000000  0.00000000       0.00000000        0.0000000
## 1519  1.00000000  0.46666667       0.00000000        0.0000000
## 1521  0.00000000  0.00000000       0.00000000        0.0000000
## 1523 -1.00000000 -0.50000000       0.00000000        0.0000000
## 1525  1.00000000  0.50000000       0.00000000       -0.1574143
## 1527  0.00000000  0.64705882       0.00000000        0.0000000
## 1529  0.00000000 -0.15384615       0.00000000       -0.5683137
## 1531 -0.17647059  0.37931034       0.91737740        0.2390253
## 1533 -0.11111111 -0.07692308       0.00000000        0.0000000
## 1535 -1.00000000  0.26315789       0.00000000        0.0000000
## 1537  0.40000000  0.07142857       0.00000000        0.0000000
## 1539  1.00000000  1.00000000       0.67125349        0.0000000
## 1541  0.20000000 -0.16666667       0.00000000       -0.5840688
## 1543  0.25000000  0.58333333       0.24875983        0.2416699
## 1545 -1.00000000 -0.22222222       0.00000000        0.0000000
## 1547  0.00000000  0.28571429       0.00000000        0.0000000
## 1549 -0.23076923 -0.15789474      -0.49096995       -0.6651225
## 1551  0.00000000 -1.00000000      -0.76379395       -0.2386342
## 1553 -1.00000000 -1.00000000      -0.17700329        0.0000000
## 1555  0.00000000  0.06666667       0.00000000        0.0000000
## 1557 -1.00000000  1.00000000       0.00000000        0.0000000
## 1559 -1.00000000  0.69230769       0.00000000        0.0000000
## 1561  0.00000000 -1.00000000       0.00000000        0.0000000
## 1563  0.00000000  1.00000000       0.00000000        0.0000000
## 1565  0.00000000 -1.00000000       0.00000000        0.0000000
## 1567  0.00000000  0.00000000       0.00000000        0.0000000
## 1569  0.00000000  0.00000000       0.00000000        0.0000000
## 1571  0.00000000  0.00000000       0.00000000        0.0000000
## 1573  0.00000000  0.00000000       0.00000000        0.0000000
## 1575  0.00000000  0.00000000       0.00000000        0.0000000
## 1577  0.00000000  0.00000000       0.00000000        0.0000000
## 1579  0.00000000  1.00000000       0.00000000        0.0000000
## 1581  0.00000000 -0.33333333       0.00000000        0.0000000
## 1583  0.00000000  1.00000000       0.00000000        0.0000000
## 1585  0.00000000  0.00000000       0.00000000        0.0000000
## 1587  1.00000000  0.33333333       0.00000000        0.0000000
## 1589 -1.00000000 -0.60000000       0.00000000        0.0000000
## 1591  0.00000000  0.00000000       0.00000000        0.0000000
## 1593  0.00000000  0.00000000       0.00000000        0.0000000
## 1595  0.00000000  0.00000000       0.00000000        0.0000000
## 1597  0.33333333  0.00000000       0.00000000        0.7556222
## 1599  0.00000000  0.00000000       0.00000000        0.0000000
## 1601  0.00000000  0.00000000       0.00000000        0.0000000
## 1603  0.00000000  0.00000000       0.00000000        0.0000000
## 1605  0.00000000  0.00000000       0.00000000        0.0000000
## 1607  0.00000000  0.00000000       0.00000000        0.0000000
## 1609  0.00000000 -0.33333333       0.00000000        0.0000000
## 1611  0.00000000  0.00000000       0.00000000        0.0000000
## 1613  0.00000000  0.00000000       0.00000000        0.0000000
## 1615  0.00000000  0.00000000       0.00000000        0.0000000
## 1617  0.00000000  0.00000000       0.00000000        0.0000000
## 1619  0.00000000  0.00000000       0.00000000        0.0000000
## 1621  0.00000000  1.00000000       0.00000000        0.0000000
## 1623  0.00000000  0.00000000       0.00000000        0.0000000
## 1625  0.00000000  0.00000000       0.00000000        0.0000000
## 1627  0.00000000  0.00000000       0.00000000        0.0000000
## 1629  0.00000000 -1.00000000       0.00000000        0.0000000
## 1631  0.00000000  0.00000000       0.00000000        0.0000000
## 1633  0.00000000  0.00000000       0.00000000        0.0000000
## 1635  0.00000000  1.00000000       0.00000000        0.0000000
## 1637  0.00000000  0.00000000       0.00000000        0.0000000
## 1639  0.00000000  0.00000000       0.00000000        0.0000000
## 1641  0.00000000 -1.00000000       0.00000000        0.0000000
## 1643  0.00000000  0.00000000       0.00000000        0.0000000
## 1645  0.00000000  1.00000000       0.00000000        0.0000000
## 1647  0.00000000 -1.00000000       0.00000000        0.0000000
## 1649  0.00000000  1.00000000       0.00000000        0.0000000
## 1651  0.00000000  0.00000000       0.00000000        0.0000000
## 1653  0.00000000 -1.00000000       0.00000000        0.0000000
## 1655  0.00000000 -1.00000000       0.00000000        0.0000000
## 1657  0.00000000 -1.00000000       0.00000000        0.0000000
## 1659  0.00000000  1.00000000       0.00000000        0.0000000
## 1661  0.00000000  0.00000000       0.00000000        0.0000000
## 1663  0.00000000  0.00000000       0.00000000        0.0000000
## 1665  0.00000000 -1.00000000       0.00000000        0.0000000
## 1667  0.00000000  0.00000000       0.00000000        0.0000000
## 1669  0.00000000  0.00000000       0.00000000        0.0000000
## 1671  0.00000000  0.33333333       0.00000000        0.0000000
## 1673  0.00000000 -1.00000000       0.00000000        0.0000000
## 1675  0.00000000 -1.00000000       0.00000000        0.0000000
## 1677  0.00000000  1.00000000       0.00000000        0.0000000
## 1679  0.00000000  0.00000000       0.00000000        0.0000000
##      Salvia.biomass phyto.biomass
## 1      0.0000000000   0.747647100
## 3      0.4915877280   0.453979280
## 5     -0.7661290323   0.230902778
## 7     -0.2856790732   0.619769646
## 9      0.0000000000  -0.299768964
## 11    -0.6999737739   0.566971742
## 13     0.0000000000   1.000000000
## 15     0.6817239950   0.429615826
## 17     0.0000000000   0.350905252
## 19    -0.5124523879   0.870535714
## 21     0.6235305500   0.588864691
## 23     0.0000000000   1.000000000
## 25     0.0000000000   0.065478574
## 27    -0.8974191441   0.524216814
## 29     0.0000000000   0.448247616
## 31     0.0000000000   0.957088264
## 33    -0.0375000000  -0.830256269
## 35    -0.0514342235  -0.600166771
## 37     0.0000000000  -1.000000000
## 39     0.0378578024   0.886589719
## 41    -0.3993929655   0.639721510
## 43     0.7294994675   0.871737676
## 45     0.0000000000   1.000000000
## 47    -0.3539423411  -0.210751467
## 49    -0.9256596558  -0.307540507
## 51     0.0000000000   0.833875259
## 53     0.3695314924   0.712384996
## 55    -0.9270633397   0.333235077
## 57     0.1777575205   0.715042521
## 59    -0.8047852094  -0.854076784
## 61     0.0000000000  -0.043541015
## 63     0.0000000000  -1.000000000
## 65     0.0000000000   1.000000000
## 67     0.0000000000   1.000000000
## 69    -0.5131930033   0.136856369
## 71    -0.8021820783  -0.341320554
## 73     0.4543795620  -0.806666801
## 75     0.3292255319   0.329225532
## 77     0.0000000000  -1.000000000
## 79     0.0000000000   0.126779479
## 81     0.0000000000   1.000000000
## 83     0.2601536773   0.260153677
## 85     0.0000000000   0.339699074
## 87     0.0000000000   0.000000000
## 89     0.0000000000   1.000000000
## 91     0.0000000000   1.000000000
## 93     0.3189017951  -0.451350758
## 95    -0.6312625251  -0.247637051
## 97     0.0000000000   0.274733160
## 99     0.0000000000   1.000000000
## 101    0.2033542977   0.203354298
## 103    0.4532591607   0.457230326
## 105    0.0000000000  -1.000000000
## 107    0.0000000000   0.451819742
## 109    0.0000000000  -1.000000000
## 111    0.0000000000   0.332104146
## 113    0.0000000000  -0.005929854
## 115    0.0000000000   0.074030243
## 117    0.0000000000   0.589261745
## 119    0.0000000000   0.000000000
## 121    0.0000000000   0.000000000
## 123    0.0000000000  -1.000000000
## 125    0.0000000000  -1.000000000
## 127   -0.4517241379  -0.253358925
## 129    0.0000000000   0.485971597
## 131    0.0000000000  -1.000000000
## 133    0.0000000000   0.000000000
## 135    0.0000000000   1.000000000
## 137    0.0000000000   1.000000000
## 139    0.0000000000   0.000000000
## 141    0.0000000000   0.000000000
## 143    0.0000000000   0.000000000
## 145    0.0000000000   0.678373383
## 147    0.5482093664   0.435504470
## 149    0.0000000000  -1.000000000
## 151    0.0000000000  -1.000000000
## 153    0.0000000000   1.000000000
## 155    0.0000000000   1.000000000
## 157    0.0000000000   0.000000000
## 159    0.0000000000   1.000000000
## 161    0.0000000000   1.000000000
## 163    0.0000000000   1.000000000
## 165    0.0000000000   0.000000000
## 167    0.0000000000   0.520547945
## 169    0.0000000000  -0.644612476
## 171    0.0000000000   1.000000000
## 173    0.0000000000   0.258426966
## 175    0.0000000000  -0.388127854
## 177    0.0000000000   0.000000000
## 179    0.0000000000   0.000000000
## 181    0.0000000000   0.488318431
## 183    0.0000000000   1.000000000
## 185    0.0000000000  -0.704918033
## 187    0.0000000000   1.000000000
## 189    0.0000000000   1.000000000
## 191    0.0000000000   1.000000000
## 193    0.0000000000   0.627473807
## 195    0.0000000000  -1.000000000
## 197    0.0000000000  -1.000000000
## 199    0.0000000000   0.106918239
## 201    0.0000000000   0.202137998
## 203    0.0000000000  -0.693002961
## 205    0.0000000000  -0.359020158
## 207    0.0000000000   0.906206897
## 209    0.0000000000  -1.000000000
## 211    0.0000000000   0.000000000
## 213    0.0000000000   1.000000000
## 215    0.0000000000   0.844951547
## 217    0.0000000000  -0.744986054
## 219    0.0000000000   1.000000000
## 221    0.0000000000  -0.657517900
## 223    0.0000000000   0.365140254
## 225    0.0000000000   0.949928469
## 227    0.0000000000   0.000000000
## 229    0.0000000000  -0.623193609
## 231    0.0000000000  -0.065240390
## 233    0.0000000000   1.000000000
## 235    0.0000000000   1.000000000
## 237    0.0000000000   1.000000000
## 239    0.0000000000   0.865997850
## 241    0.0000000000   1.000000000
## 243    0.0000000000  -1.000000000
## 245    0.0000000000  -1.000000000
## 247    0.0000000000  -0.316666667
## 249    0.0000000000   0.000000000
## 251    0.0000000000   1.000000000
## 253    0.0000000000   0.000000000
## 255    0.0000000000   0.000000000
## 257    0.0000000000   0.000000000
## 259    0.0000000000   0.000000000
## 261    0.0000000000   0.000000000
## 263    0.0000000000   0.000000000
## 265    0.0000000000   0.000000000
## 267    0.0000000000   0.000000000
## 269    0.0000000000   0.000000000
## 271    0.0000000000   0.000000000
## 273    0.0000000000   0.000000000
## 275    0.0000000000   1.000000000
## 277    0.0000000000   0.000000000
## 279    0.0000000000   0.093750000
## 281    0.0000000000   0.000000000
## 283    0.0000000000  -0.248847926
## 285    0.0000000000   0.000000000
## 287    0.0000000000   0.551515152
## 289    0.0000000000   0.000000000
## 291    0.0000000000   0.000000000
## 293    0.0000000000   0.000000000
## 295    0.0000000000   0.000000000
## 297    0.0000000000   0.000000000
## 299    0.0000000000   0.000000000
## 301    0.0000000000  -0.067164179
## 303   -0.8230179028  -0.483763530
## 305    0.0000000000  -1.000000000
## 307    0.0000000000   1.000000000
## 309    0.0000000000   0.647972390
## 311   -0.9094076655   0.349940688
## 313    0.0000000000   1.000000000
## 315    0.0000000000   0.453781513
## 317    0.0000000000  -0.290322581
## 319    0.0000000000   0.745182013
## 321    0.0000000000   1.000000000
## 323    0.3370629371   0.337062937
## 325    0.0000000000   0.000000000
## 327    0.0000000000  -1.000000000
## 329    0.0000000000  -1.000000000
## 331    0.0000000000   1.000000000
## 333    0.0000000000  -1.000000000
## 335    0.0000000000  -0.846796657
## 337    0.0000000000   0.936562001
## 339   -0.7823585810  -0.710998619
## 341    0.3703148426   0.255172414
## 343   -0.5817223199   0.209833187
## 345    0.0000000000  -1.000000000
## 347    0.0000000000   0.398773006
## 349    0.0000000000   0.000000000
## 351    0.0000000000   1.000000000
## 353    0.0000000000   0.000000000
## 355    0.0000000000   1.000000000
## 357    0.0000000000   0.000000000
## 359    0.0000000000   0.000000000
## 361    0.0000000000   1.000000000
## 363    0.3345864662   0.410981697
## 365    0.0000000000   1.000000000
## 367    0.0000000000   0.000000000
## 369    0.8353065380   0.835306538
## 371    0.0000000000   1.000000000
## 373    0.0000000000   0.000000000
## 375    0.0000000000   1.000000000
## 377   -0.5988710743  -0.551993472
## 379    0.7348052592   0.734805259
## 381   -0.4981525025  -0.498152503
## 383    0.0000000000  -1.000000000
## 385   -0.9168611547  -0.916861155
## 387   -0.3601232812  -0.360123281
## 389    0.0000000000   1.000000000
## 391   -0.0094212651  -0.009421265
## 393    0.2793959008   0.847939904
## 395    0.0000000000  -1.000000000
## 397    0.6370209689   0.637020969
## 399    0.0000000000   1.000000000
## 401    0.0000000000  -1.000000000
## 403   -0.0006049607  -0.047948855
## 405    0.0000000000   0.000000000
## 407   -0.7111834962  -0.730860034
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## 1493  -0.6889282355  -0.139693493
## 1495   0.0000000000   1.000000000
## 1497   0.0000000000  -1.000000000
## 1499   0.0000000000   1.000000000
## 1501   0.8894411396   0.844093692
## 1503   0.9223154132   0.952800635
## 1505   0.2089055482   0.477723849
## 1507   0.2858314538   0.575594967
## 1509   0.0000000000  -0.933890496
## 1511   0.5503928937   0.529470529
## 1513   0.0000000000   1.000000000
## 1515  -0.4843110505  -0.618567104
## 1517   0.0133924076   0.358601728
## 1519   0.0000000000  -1.000000000
## 1521   0.0000000000   0.000000000
## 1523   0.0000000000   1.000000000
## 1525  -0.1514016720  -0.059505024
## 1527   0.7111170497   0.291708695
## 1529  -0.3966559315  -0.049463979
## 1531   0.4587267332   0.559456486
## 1533  -0.0288499619  -0.028849962
## 1535   0.8360339506   0.836033951
## 1537  -0.6072351421  -0.115977004
## 1539  -0.0730430230   0.299704004
## 1541  -0.5058375727   0.555315194
## 1543   0.8788083700   0.561749105
## 1545  -0.6367938194  -0.582754654
## 1547   0.4034753630   0.343154378
## 1549  -0.2324228685  -0.385799656
## 1551   0.0000000000  -0.703697855
## 1553   0.0000000000  -0.423311012
## 1555   0.2390806703   0.250541126
## 1557   0.0000000000   0.458159501
## 1559   0.5317562355  -0.036512879
## 1561   0.0000000000  -1.000000000
## 1563   0.0000000000   1.000000000
## 1565   0.0000000000  -1.000000000
## 1567   0.0000000000   0.000000000
## 1569   0.0000000000   0.000000000
## 1571   0.0000000000   0.000000000
## 1573   0.0000000000   0.000000000
## 1575   0.0000000000   0.000000000
## 1577   0.0000000000   0.000000000
## 1579   0.8346394984   0.834639498
## 1581   0.5702535984   0.655305113
## 1583   0.0000000000  -1.000000000
## 1585   0.0000000000   1.000000000
## 1587   0.4154309048   0.542010439
## 1589  -0.6098971290  -0.788502898
## 1591   0.0000000000   1.000000000
## 1593   0.8129070456   0.870119194
## 1595   0.0000000000   0.000000000
## 1597   0.0000000000   0.755622189
## 1599  -0.0209205021  -0.020920502
## 1601   0.0000000000   0.000000000
## 1603   0.0000000000   1.000000000
## 1605   0.0000000000   0.000000000
## 1607   0.0000000000   0.000000000
## 1609   0.3221925134   0.322192513
## 1611   0.0000000000   0.000000000
## 1613   0.0000000000   0.000000000
## 1615   0.0000000000   0.000000000
## 1617   0.0000000000   0.000000000
## 1619   0.0000000000   0.000000000
## 1621   0.0000000000   0.000000000
## 1623   0.0000000000   0.000000000
## 1625   0.0869891264   0.000000000
## 1627   0.0000000000   0.000000000
## 1629   0.0000000000  -1.000000000
## 1631   0.0000000000   0.000000000
## 1633   0.0000000000   0.000000000
## 1635   0.0000000000   0.000000000
## 1637   0.0000000000   0.000000000
## 1639   0.0000000000   0.000000000
## 1641   0.0000000000   0.000000000
## 1643   0.6618220489  -1.000000000
## 1645   0.0000000000   0.000000000
## 1647   0.0000000000   1.000000000
## 1649   0.0000000000   0.000000000
## 1651   0.0000000000   0.000000000
## 1653   0.0000000000  -1.000000000
## 1655   0.0000000000  -1.000000000
## 1657   0.0000000000   0.000000000
## 1659   0.0000000000   0.000000000
## 1661   0.0000000000   0.000000000
## 1663   0.0000000000   0.000000000
## 1665   0.0000000000   0.000000000
## 1667   0.0000000000   0.000000000
## 1669   0.0000000000   0.000000000
## 1671   0.4023856258   0.000000000
## 1673   0.0000000000   0.000000000
## 1675   0.0000000000   0.000000000
## 1677   0.0000000000   0.000000000
## 1679   0.0000000000   0.000000000
rii.mean <- rii.data %>% group_by(Year, Gradient,Site) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
rii.mean <- data.frame(rii.mean)

season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s1.mean <- data.frame(season1.mean)

season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s2.mean <- data.frame(season2.mean)


s1.mean[,"arid.gradient"] <- log(s1.mean[,"Precip"]/arid.vals[,"PET"])
s2.mean[,"arid.gradient"] <- log(s2.mean[,"Precip"]/(arid.vals[,"PET"]))

## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- as.factor(c(rep("2016",7),rep("2017",7)))

## combine climate data with census
mean.phyto  <- merge(mean.phyto , site.climate, by=c("Year","Site"))

phyto.2016 <- subset(mean.phyto, Year==2016)

plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],1), phyto.2016[phyto.2016$Microsite=="open","phyto.biomass"], ylim=c(0,2))
points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],2), phyto.2016[phyto.2016$Microsite=="shrub","phyto.biomass"], pch=19)

plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"], rii.mean[rii.mean$Year==2016,"phyto.biomass"], ylim=c(-0.4,0.4))

phyto.2017 <- subset(mean.phyto, Year==2017)

plot(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],1), phyto.2017[phyto.2017$Microsite=="open","phyto.biomass"], ylim=c(0,3))
points(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],2), phyto.2017[phyto.2017$Microsite=="shrub","phyto.biomass"], pch=19)

## Phacelia season 1
plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
points(jitter(phyto.2016[phyto.2016$Microsite=="shrub","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Phacelia.biomass"], pch=19)

m1 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2016)
m2 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2016)
m3 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2016)

plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Phacelia.biomass"], ylim=c(-0.4,0.4))

## Plantago season 1
plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Plantago.biomass"], pch=19)

plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Plantago.biomass"], ylim=c(-0.4,0.4))

## Salvia season 1
plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Salvia.biomass"], pch=19)

plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Salvia.biomass"], ylim=c(-0.4,0.4))

## Phacelia season 2
plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Phacelia.biomass"], pch=19)

plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Phacelia.biomass"], ylim=c(-0.4,0.4))

## Plantago season 2
plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Plantago.biomass"], pch=19)

plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Plantago.biomass"], ylim=c(-0.4,0.4))

## Salvia season 2
plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Salvia.biomass"], pch=19)

plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Salvia.biomass"], ylim=c(-0.4,0.4))

## both seasons


## responses
plot(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"], mean.phyto[mean.phyto$Microsite=="open","phyto.biomass"], ylim=c(0,3))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="shrub","phyto.biomass"], pch=19)

## Phacelia
plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Phacelia.biomass"], pch=19)

m1 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
summary(m1)
## 
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite, 
##     data = mean.phyto)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65182 -0.26707 -0.07056  0.19696  0.93445 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   0.99664    0.48849   2.040   0.0547 .
## arid.gradient                 0.19075    0.14775   1.291   0.2114  
## Micrositeshrub                0.39178    0.63260   0.619   0.5427  
## arid.gradient:Micrositeshrub  0.09948    0.19538   0.509   0.6162  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3984 on 20 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.2704, Adjusted R-squared:  0.161 
## F-statistic: 2.471 on 3 and 20 DF,  p-value: 0.0914
## Plantago 
plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], ylim=c(0,0.5))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Plantago.biomass"], pch=19)

m2 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
summary(m2)
## 
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite, 
##     data = mean.phyto)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65182 -0.26707 -0.07056  0.19696  0.93445 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   0.99664    0.48849   2.040   0.0547 .
## arid.gradient                 0.19075    0.14775   1.291   0.2114  
## Micrositeshrub                0.39178    0.63260   0.619   0.5427  
## arid.gradient:Micrositeshrub  0.09948    0.19538   0.509   0.6162  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3984 on 20 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.2704, Adjusted R-squared:  0.161 
## F-statistic: 2.471 on 3 and 20 DF,  p-value: 0.0914
## Salvia season 1
plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Salvia.biomass"], pch=19)

m3 <- lm(ihs(Salvia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
summary(m3)
## 
## Call:
## lm(formula = ihs(Salvia.biomass) ~ arid.gradient * Microsite, 
##     data = mean.phyto)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.32935 -0.17295 -0.09718  0.12934  0.75775 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   0.672545   0.293225   2.294   0.0309 *
## arid.gradient                 0.103841   0.095522   1.087   0.2878  
## Micrositeshrub               -0.026494   0.414683  -0.064   0.9496  
## arid.gradient:Micrositeshrub  0.009403   0.135088   0.070   0.9451  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.317 on 24 degrees of freedom
## Multiple R-squared:  0.1042, Adjusted R-squared:  -0.00779 
## F-statistic: 0.9304 on 3 and 24 DF,  p-value: 0.4412
### Rii plots

rii.mean <- merge(rii.mean, site.climate, by=c("Year","Site"))


## biomass by RII
plot(mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.2,0.2), pch=19)
points(mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)

## rii variability


plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.5,0.5), pch=19)
points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)



## compare convex hull
## hull function
Plot_ConvexHull<-function(xcoord, ycoord, lcolor){
  hpts <- chull(x = xcoord, y = ycoord)
  hpts <- c(hpts, hpts[1])
  lines(xcoord[hpts], ycoord[hpts], col = lcolor)
}  

Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Phacelia.biomass"], lcolor = "black")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Plantago.biomass"], lcolor = "blue")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Salvia.biomass"], lcolor = "red")

r.df <- rii.mean[,c("arid.gradient","Phacelia.biomass","Plantago.biomass","Salvia.biomass")]


## transform data into long format
r.df <- gather(r.df, species, biomass, 2:4)


## function to create a convex hull around data
hull.time <- function(df){
  df[chull(df$arid.gradient,df$biomass),]  # chull really is useful, even outside of contrived examples.
  }

## break data into lists and recombined to apply polygon
splitData <- split(r.df, r.df$species)
appliedData <- lapply(splitData, hull.time)
combinedData <- do.call(rbind, appliedData)

## create polygon 
poly1 <- ggplot(r.df, aes(x=arid.gradient, y=biomass))+
geom_polygon(data = combinedData,  # This is also a nice example of how to plot
                          aes(x=arid.gradient, y=biomass, group = species, color=species), fill=NA,  # two superimposed geoms
                          alpha = 1/2)  + theme_bw()    
poly1

m1 <- lm(Phacelia.biomass~arid.gradient, data=rii.mean)
m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)


m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))


m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))


plot(rii.mean[,"min.temp"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
points(rii.mean[,"min.temp"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(rii.mean[,"min.temp"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)

rii.test <- subset(rii.mean, Gradient.x>3 & Year == 2017 | Year == 2016)


plot(rii.test[,"arid.gradient"], rii.test[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
points(rii.test[,"arid.gradient"], rii.test[,"Plantago.biomass"], pch=21, bg="blue")
points(rii.test[,"arid.gradient"], rii.test[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)

plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia"], ylim=c(-0.4,0.4), pch=19)
points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago"], pch=21, bg="blue")
points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)

m1 <- lm(Phacelia~ poly(arid.gradient,2), data=rii.mean)
summary(m1)
## 
## Call:
## lm(formula = Phacelia ~ poly(arid.gradient, 2), data = rii.mean)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.212432 -0.092479 -0.001991  0.065314  0.284280 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.13789    0.03724   3.703  0.00348 **
## poly(arid.gradient, 2)1  0.21332    0.13934   1.531  0.15402   
## poly(arid.gradient, 2)2 -0.05320    0.13934  -0.382  0.70986   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1393 on 11 degrees of freedom
## Multiple R-squared:  0.1846, Adjusted R-squared:  0.03629 
## F-statistic: 1.245 on 2 and 11 DF,  p-value: 0.3256
m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)

Differences from Nutrient content

## Nitrogen difference between shrub and sites
m.nit <- aov(log(N) ~ site * microsite, data=nutrients)
summary(m.nit)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## site            6  63.04   10.51  13.241 2.86e-09 ***
## microsite       1  40.16   40.16  50.614 2.24e-09 ***
## site:microsite  6   4.96    0.83   1.042    0.408    
## Residuals      56  44.43    0.79                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.nit, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(N) ~ site * microsite, data = nutrients)
## 
## $microsite
##                diff      lwr      upr p adj
## shrub-open 1.514873 1.088317 1.941429     0
## Potassium difference between shrub and sites
m.pot <- aov(log(K) ~ site * microsite, data=nutrients)
summary(m.pot)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## site            6 15.234   2.539   20.46 1.62e-12 ***
## microsite       1  4.019   4.019   32.39 4.80e-07 ***
## site:microsite  6  1.571   0.262    2.11   0.0664 .  
## Residuals      56  6.948   0.124                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.pot, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(K) ~ site * microsite, data = nutrients)
## 
## $microsite
##                 diff      lwr       upr p adj
## shrub-open 0.4792314 0.310559 0.6479038 5e-07
## Phosphorus difference between shrub and sites
m.pho <- aov(log(P) ~ site * microsite, data=nutrients)
summary(m.pho)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## site            6 23.163   3.861   14.33 8.10e-10 ***
## microsite       1 10.546  10.546   39.15 5.79e-08 ***
## site:microsite  6  3.394   0.566    2.10   0.0676 .  
## Residuals      56 15.085   0.269                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.pho, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(P) ~ site * microsite, data = nutrients)
## 
## $microsite
##                 diff       lwr      upr p adj
## shrub-open 0.7762734 0.5277317 1.024815 1e-07

annual plant community repsonse

spp.data  <- read.csv("Data/ERG.communitydata.csv")
spp.data[is.na(spp.data)] <- 0

mean.spp <- spp.data %>% group_by(Year,Site, Microsite) %>%  summarize(abd=mean(Abundance), richness=mean(Richness), biomass=mean(Biomass))
mean.spp <- data.frame(mean.spp)

## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- c(rep("2016",7),rep("2017",7))

## combine climate data with community data
mean.spp <- merge(mean.spp, site.climate, by.y=c("Year","Site"))

## community responses season1
mean.spp2016 <- subset(mean.spp, Year==2016)

## richness
plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","richness"])
points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","richness"], pch=19) 

m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2016)
summary(m1)
## 
## Call:
## lm(formula = richness ~ arid.gradient * Microsite, data = mean.spp2016)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2665 -0.8572 -0.1081  0.5670  1.9222 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                    5.6948     1.5206   3.745  0.00381 **
## arid.gradient                  1.0347     0.4360   2.373  0.03908 * 
## Micrositeshrub                -1.6888     2.1504  -0.785  0.45045   
## arid.gradient:Micrositeshrub  -0.4223     0.6166  -0.685  0.50903   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.136 on 10 degrees of freedom
## Multiple R-squared:  0.4385, Adjusted R-squared:  0.2701 
## F-statistic: 2.604 on 3 and 10 DF,  p-value: 0.1099
## abundance
plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","abd"])
points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","abd"], pch=19) 

m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2016)
summary(m2)
## 
## Call:
## lm(formula = abd ~ arid.gradient * Microsite, data = mean.spp2016)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -60.072 -16.405  -3.899  12.072  66.172 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                  168.9575    47.2296   3.577  0.00503 **
## arid.gradient                 39.4634    13.5432   2.914  0.01546 * 
## Micrositeshrub                 1.4860    66.7928   0.022  0.98269   
## arid.gradient:Micrositeshrub   0.1766    19.1530   0.009  0.99282   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35.29 on 10 degrees of freedom
## Multiple R-squared:  0.6304, Adjusted R-squared:  0.5196 
## F-statistic: 5.687 on 3 and 10 DF,  p-value: 0.01551
## biomass
plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","biomass"])
points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","biomass"], pch=19) 

m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2016)
summary(m3)
## 
## Call:
## lm(formula = biomass ~ arid.gradient * Microsite, data = mean.spp2016)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.4738 -1.2765  0.3059  1.3226  9.5119 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)
## (Intercept)                     6.869      5.574   1.232    0.246
## arid.gradient                   1.562      1.598   0.977    0.351
## Micrositeshrub                 11.863      7.883   1.505    0.163
## arid.gradient:Micrositeshrub    2.901      2.260   1.283    0.228
## 
## Residual standard error: 4.165 on 10 degrees of freedom
## Multiple R-squared:  0.4922, Adjusted R-squared:  0.3398 
## F-statistic: 3.231 on 3 and 10 DF,  p-value: 0.0693
##season2

## community responses season2

mean.spp2017 <- subset(mean.spp, Year==2017)


## richness
plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","richness"])
points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","richness"], pch=19) 

m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2017)
summary(m1)
## 
## Call:
## lm(formula = richness ~ arid.gradient * Microsite, data = mean.spp2017)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6804 -0.2471 -0.1016  0.2195  1.0560 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    4.8947     1.0446   4.686  0.00086 ***
## arid.gradient                  0.9476     0.4040   2.345  0.04096 *  
## Micrositeshrub                -4.5766     1.4773  -3.098  0.01129 *  
## arid.gradient:Micrositeshrub  -1.7020     0.5714  -2.979  0.01383 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5585 on 10 degrees of freedom
## Multiple R-squared:  0.4946, Adjusted R-squared:  0.343 
## F-statistic: 3.262 on 3 and 10 DF,  p-value: 0.06779
## abundance
plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","abd"])
points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","abd"], pch=19) 

m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2017)
summary(m2)
## 
## Call:
## lm(formula = abd ~ arid.gradient * Microsite, data = mean.spp2017)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.959 -21.846  -7.899  15.547  71.744 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                    259.98      68.16   3.814  0.00341 **
## arid.gradient                   84.60      26.36   3.209  0.00934 **
## Micrositeshrub                  96.88      96.39   1.005  0.33855   
## arid.gradient:Micrositeshrub    30.94      37.28   0.830  0.42602   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.44 on 10 degrees of freedom
## Multiple R-squared:  0.7526, Adjusted R-squared:  0.6783 
## F-statistic: 10.14 on 3 and 10 DF,  p-value: 0.002236
## biomass
plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","biomass"])
points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","biomass"], pch=19) 

m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2017)
summary(m3)
## 
## Call:
## lm(formula = biomass ~ arid.gradient * Microsite, data = mean.spp2017)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9854 -1.0830 -0.1588  0.5636  6.6591 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                     6.713      5.431   1.236   0.2447  
## arid.gradient                   2.161      2.100   1.029   0.3279  
## Micrositeshrub                 15.743      7.680   2.050   0.0675 .
## arid.gradient:Micrositeshrub    5.061      2.971   1.704   0.1193  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.903 on 10 degrees of freedom
## Multiple R-squared:  0.6218, Adjusted R-squared:  0.5083 
## F-statistic: 5.479 on 3 and 10 DF,  p-value: 0.01733
## both seasons

## richness
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","richness"])
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","richness"], pch=19) 

m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = richness ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56169 -0.66225 -0.07639  0.66154  1.76433 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    4.9206     0.8381   5.871 4.68e-06 ***
## arid.gradient                  0.8699     0.2730   3.186  0.00397 ** 
## Micrositeshrub                -1.8968     1.1852  -1.600  0.12260    
## arid.gradient:Micrositeshrub  -0.5531     0.3861  -1.432  0.16490    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9061 on 24 degrees of freedom
## Multiple R-squared:  0.3356, Adjusted R-squared:  0.2526 
## F-statistic: 4.042 on 3 and 24 DF,  p-value: 0.01849
## abundance
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","abd"])
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","abd"], pch=19) 

m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp)
summary(m2)
## 
## Call:
## lm(formula = abd ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -64.997 -26.611  -5.081  25.895 108.541 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    162.65      38.28   4.249  0.00028 ***
## arid.gradient                   41.28      12.47   3.310  0.00294 ** 
## Micrositeshrub                  39.22      54.14   0.724  0.47582    
## arid.gradient:Micrositeshrub    10.04      17.64   0.569  0.57457    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.39 on 24 degrees of freedom
## Multiple R-squared:  0.5409, Adjusted R-squared:  0.4835 
## F-statistic: 9.426 on 3 and 24 DF,  p-value: 0.000268
## biomass
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], ihs(mean.spp[mean.spp$Microsite=="open","biomass"]))
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],ihs(mean.spp[mean.spp$Microsite=="shrub","biomass"]), pch=19)

m3 <- lm(ihs(biomass)~arid.gradient*Microsite, data=mean.spp)
summary(m3)
## 
## Call:
## lm(formula = ihs(biomass) ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6596 -0.5085 -0.0684  0.3314  1.2077 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    2.6251     0.5577   4.707 8.75e-05 ***
## arid.gradient                  0.5873     0.1817   3.233  0.00355 ** 
## Micrositeshrub                 1.6603     0.7888   2.105  0.04596 *  
## arid.gradient:Micrositeshrub   0.3698     0.2569   1.439  0.16303    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.603 on 24 degrees of freedom
## Multiple R-squared:  0.6498, Adjusted R-squared:  0.606 
## F-statistic: 14.84 on 3 and 24 DF,  p-value: 1.128e-05

cleaned up community response to gradient

RDA community and environmental data

community <- read.csv("Data/ERG.communitydata.csv")
community[is.na(community)] <- 0

##2016
## add enviro data to dataframe
comm2016 <- subset(community, Year==2016)
site.vars2016[,"Site"] <- as.factor(row.names(site.vars2016))
comm2016 <- merge(comm2016, site.vars2016, by="Site")

## transform spp data
community.trans <- decostand(comm2016[,13:53], "hellinger")

rda1 <- rda(community.trans, comm2016[,54:57])
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained
## [1] 0.3871872
## build byplot manually
par(mar=c(4.5,4.5,0.5,0.5))
plot(rda1, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))

## calculate priority
spp.priority <- colSums(comm2016[,13:53])

## plot RDA1
colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
points(rda1, display = "sites", col = "black", pch = c(21), bg = colvec)
orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
text(rda1,  display = "bp", col = "blue", cex = 0.8) 
legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)

## ordikplot to adjust species locations

## 2017
## add enviro data to dataframe
comm2017 <- subset(community, Year==2017)
site.vars[,"Site"] <- as.factor(row.names(site.vars))
comm2017 <- merge(comm2017, site.vars, by="Site")
# 
# ## transform spp data
# community.trans <- decostand(comm2017[,13:53], "hellinger")
# 
# rda2 <- rda(community.trans, comm2017[,54:58])
# (R2adj <- RsquareAdj(rda2)$adj.r.squared) ## 50.4% of variation explained
# 
# 
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda2, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
# 
# ## calculate priority
# spp.priority <- colSums(comm2017[,13:53])
# 
# ## plot RDA2
# colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
# points(rda2, display = "sites", col = "black", pch = c(21), bg = colvec)
# orditorp(rda2, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
# text(rda2,  display = "bp", col = "blue", cex = 0.8) 
# legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
# ## ordikplot to adjust species locations

Phytometer analysis

census <- read.csv("Data/ERG.phytometer.census.csv")
census[is.na(census)] <- 0
census[,"phyto.abd"] <- rowSums(census[,c("Phacelia","Plantago","Salvia")])

## calculate the number of seeds per gram
seed.mass <- read.csv("Data/seed.mass.csv")
seed.mass.avg <- seed.mass %>% group_by(species) %>%  summarize(avg.seed.gram=mean(seed.number),se.seed.gram=se(seed.number))
seed.mass.avg <- data.frame(seed.mass.avg)
seed.mass.avg
##      species avg.seed.gram se.seed.gram
## 1   Amsinkia         235.2     7.611833
## 2 Caulanthus        3019.0     8.916277
## 3  Lipidium          752.4     4.261455
## 4 Monolopia          737.8     7.831986
## 5   Phacelia         772.0     5.300943
## 6   Plantago         558.2     6.865858
## 7     Salvia         980.0     9.612492
## get proportion of seed germinated per 0.3 grams of seed
census[,"Phacelia.prop"] <- census[,"Phacelia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Phacelia")]*0.3)
census[,"Plantago.prop"] <- census[,"Plantago"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Plantago")]*0.3)
census[,"Salvia.prop"] <- census[,"Salvia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Salvia")]*0.3)


## beginning
census.int <- subset(census, Census=="emergence")

## all species
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8108), link: log
## 
## Response: Phacelia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1079.71              
## Microsite                1   10.304       838    1069.41  0.001328 ** 
## Nutrient                 1    1.234       837    1068.17  0.266538    
## Site                     6  159.651       831     908.52 < 2.2e-16 ***
## Year                     1   31.424       830     877.10 2.073e-08 ***
## Microsite:Nutrient       1    0.046       829     877.05  0.829874    
## Microsite:Site           6   21.032       823     856.02  0.001811 ** 
## Nutrient:Site            6    5.542       817     850.48  0.476389    
## Microsite:Nutrient:Site  6    8.030       811     842.45  0.235879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Phacelia
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8108), link: log
## 
## Response: Phacelia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1079.71              
## Microsite                1   10.304       838    1069.41  0.001328 ** 
## Nutrient                 1    1.234       837    1068.17  0.266538    
## Site                     6  159.651       831     908.52 < 2.2e-16 ***
## Year                     1   31.424       830     877.10 2.073e-08 ***
## Microsite:Nutrient       1    0.046       829     877.05  0.829874    
## Microsite:Site           6   21.032       823     856.02  0.001811 ** 
## Nutrient:Site            6    5.542       817     850.48  0.476389    
## Microsite:Nutrient:Site  6    8.030       811     842.45  0.235879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Plantago
m2 <- glm.nb(Plantago~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m2, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.5335), link: log
## 
## Response: Plantago
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839     984.30              
## Microsite                1    6.256       838     978.04   0.01238 *  
## Nutrient                 1    1.060       837     976.98   0.30332    
## Site                     6  162.687       831     814.30 < 2.2e-16 ***
## Year                     1    4.138       830     810.16   0.04194 *  
## Microsite:Nutrient       1    2.388       829     807.77   0.12224    
## Microsite:Site           6   43.000       823     764.77 1.167e-07 ***
## Nutrient:Site            6    6.391       817     758.38   0.38088    
## Microsite:Nutrient:Site  6    4.615       811     753.77   0.59403    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Salvia
m3 <- glm.nb(Salvia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m3, test="Chisq") ## Site * Micro and Year significant + (micro x nutrient)
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8273), link: log
## 
## Response: Salvia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1210.25              
## Microsite                1    5.924       838    1204.33 0.0149386 *  
## Nutrient                 1    1.513       837    1202.82 0.2186678    
## Site                     6  211.625       831     991.19 < 2.2e-16 ***
## Year                     1   17.463       830     973.73 2.929e-05 ***
## Microsite:Nutrient       1    7.824       829     965.90 0.0051548 ** 
## Microsite:Site           6   26.421       823     939.48 0.0001858 ***
## Nutrient:Site            6    4.587       817     934.90 0.5977624    
## Microsite:Nutrient:Site  6    1.566       811     933.33 0.9549811    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
census.int.plot <- gather(census.int, species, abundance, Phacelia:Salvia)

##calculate confidence interval
census.plot<- census.int.plot %>%  group_by(Microsite, species, Site, Year) %>%  summarize(avg=mean(abundance),ci=se(abundance)*1.96)

ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
  geom_bar(position=position_dodge(), stat="identity")+
   geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 

## end season
census.end <- subset(census, Census=="end")
census.end[,"Year"] <- as.factor(census.end$Year)

## run full model for phyto biomass
m1 <- aov(log(phyto.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, phyto.biomass>0))
summary(m1)
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  330.8   55.13  32.164  < 2e-16 ***
## Microsite                 1   10.9   10.88   6.347 0.012127 *  
## Nutrient                  1   30.3   30.25  17.651 3.24e-05 ***
## Year                      1  161.2  161.21  94.051  < 2e-16 ***
## Site:Microsite            6   23.9    3.99   2.327 0.031940 *  
## Site:Nutrient             6   41.9    6.98   4.073 0.000551 ***
## Microsite:Nutrient        1    1.7    1.73   1.011 0.315196    
## Site:Microsite:Nutrient   6    7.9    1.32   0.768 0.595068    
## Residuals               424  726.7    1.71                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m1, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(phyto.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, phyto.biomass > 0))
## 
## $Microsite
##                 diff        lwr       upr     p adj
## shrub-open 0.3096905 0.06750868 0.5518722 0.0123243
## all species end of season
m2 <- glm.nb(phyto.abd~ Microsite * Nutrient * Site + Year, data=census.end)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.455), link: log
## 
## Response: phyto.abd
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                      839    1009.51             
## Microsite                1    1.059       838    1008.45  0.30341    
## Nutrient                 1    2.527       837    1005.92  0.11194    
## Site                     6  135.420       831     870.50  < 2e-16 ***
## Year                     1    3.412       830     867.09  0.06474 .  
## Microsite:Nutrient       1    4.586       829     862.50  0.03224 *  
## Microsite:Site           6    5.340       823     857.16  0.50104    
## Nutrient:Site            6    5.169       817     852.00  0.52238    
## Microsite:Nutrient:Site  6    5.683       811     846.31  0.45956    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## run full model for phacelia biomass
m3 <- aov(log(Phacelia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia.biomass>0))
summary(m3) ## site and microsite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6   89.7   14.95   8.274 4.95e-08 ***
## Microsite                 1   21.2   21.20  11.738  0.00074 ***
## Nutrient                  1   10.2   10.19   5.642  0.01845 *  
## Year                      1   95.5   95.49  52.857 7.42e-12 ***
## Site:Microsite            6   23.6    3.93   2.173  0.04692 *  
## Site:Nutrient             6    8.0    1.34   0.742  0.61612    
## Microsite:Nutrient        1    0.0    0.00   0.001  0.97552    
## Site:Microsite:Nutrient   5    4.1    0.82   0.452  0.81122    
## Residuals               205  370.3    1.81                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m3, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
## 
## $Microsite
##                 diff       lwr       upr     p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714
## run full model for plantago biomass
m4 <- aov(log(Plantago.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago.biomass>0))
summary(m4) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6 116.35   19.39  19.843 2.32e-16 ***
## Microsite                 1   1.96    1.96   2.001    0.160    
## Nutrient                  1  32.03   32.03  32.778 6.80e-08 ***
## Year                      1 138.38  138.38 141.597  < 2e-16 ***
## Site:Microsite            6   4.87    0.81   0.830    0.549    
## Site:Nutrient             4   1.99    0.50   0.508    0.730    
## Microsite:Nutrient        1   3.17    3.17   3.244    0.074 .  
## Site:Microsite:Nutrient   3   0.65    0.22   0.220    0.882    
## Residuals               130 127.05    0.98                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m4, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823
## run full model for plantago biomass
m5 <- aov(log(Salvia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia.biomass>0))
summary(m5) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  170.1   28.35  19.982  < 2e-16 ***
## Microsite                 1    0.0    0.01   0.008 0.929221    
## Nutrient                  1   21.4   21.43  15.103 0.000124 ***
## Year                      1   44.1   44.05  31.045 5.39e-08 ***
## Site:Microsite            6    6.8    1.14   0.802 0.568988    
## Site:Nutrient             6   46.2    7.70   5.424 2.35e-05 ***
## Microsite:Nutrient        1    0.5    0.55   0.387 0.534459    
## Site:Microsite:Nutrient   6   12.9    2.16   1.520 0.171069    
## Residuals               317  449.8    1.42                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m5, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351
## run full model for phacelia abundance
m6 <- glm.nb(Phacelia.biomass~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia>0))
summary(m3) ## site and microsite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6   89.7   14.95   8.274 4.95e-08 ***
## Microsite                 1   21.2   21.20  11.738  0.00074 ***
## Nutrient                  1   10.2   10.19   5.642  0.01845 *  
## Year                      1   95.5   95.49  52.857 7.42e-12 ***
## Site:Microsite            6   23.6    3.93   2.173  0.04692 *  
## Site:Nutrient             6    8.0    1.34   0.742  0.61612    
## Microsite:Nutrient        1    0.0    0.00   0.001  0.97552    
## Site:Microsite:Nutrient   5    4.1    0.82   0.452  0.81122    
## Residuals               205  370.3    1.81                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m3, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
## 
## $Microsite
##                 diff       lwr       upr     p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714
## run full model for plantago biomass
m7 <- aov(log(Plantago)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago>0))
summary(m4) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6 116.35   19.39  19.843 2.32e-16 ***
## Microsite                 1   1.96    1.96   2.001    0.160    
## Nutrient                  1  32.03   32.03  32.778 6.80e-08 ***
## Year                      1 138.38  138.38 141.597  < 2e-16 ***
## Site:Microsite            6   4.87    0.81   0.830    0.549    
## Site:Nutrient             4   1.99    0.50   0.508    0.730    
## Microsite:Nutrient        1   3.17    3.17   3.244    0.074 .  
## Site:Microsite:Nutrient   3   0.65    0.22   0.220    0.882    
## Residuals               130 127.05    0.98                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m4, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823
## run full model for plantago biomass
m8 <- aov(log(Salvia)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia>0))
summary(m5) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  170.1   28.35  19.982  < 2e-16 ***
## Microsite                 1    0.0    0.01   0.008 0.929221    
## Nutrient                  1   21.4   21.43  15.103 0.000124 ***
## Year                      1   44.1   44.05  31.045 5.39e-08 ***
## Site:Microsite            6    6.8    1.14   0.802 0.568988    
## Site:Nutrient             6   46.2    7.70   5.424 2.35e-05 ***
## Microsite:Nutrient        1    0.5    0.55   0.387 0.534459    
## Site:Microsite:Nutrient   6   12.9    2.16   1.520 0.171069    
## Residuals               317  449.8    1.42                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m5, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351
census.end.plot <- gather(census.end, species, biomass, Phacelia.biomass:Salvia.biomass)

##calculate confidence interval
census.plot<- census.end.plot %>%  group_by(Microsite, species, Site, Year) %>%  summarize(avg=mean(biomass),ci=se(biomass)*1.96)

ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
  geom_bar(position=position_dodge(), stat="identity")+
   geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 

### RDA
# 
# ##collect environmental variables for site
# nutrients <- read.csv("Data/ERG.soilnutrients.csv")
# nutrients.mean <- nutrients %>% group_by(gradient,site, microsite) %>% summarise_each(funs(mean))
# nutrients.vars <- data.frame(nutrients.mean)
# 
# ## minimum explanation of variables
# ambient2016[is.na(ambient2016)] <- 0
# community <- ambient2016 %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(sum))
# community <- data.frame(community)
# 
# end.census <- subset(census2016, census=="end")
# end.census  <- end.census  %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(mean))
# end.census<- data.frame(end.census)
# 
# ## daily means
# day.mean <- aggregate(HOBO.data, by=list(day=HOBO.data$Day, micro=HOBO.data$Microsite, site=HOBO.data$Site), mean)
# 
# ## summarize daily means across sites
# means <- day.mean %>% group_by(gradient, micro) %>% summarize(temp=mean(Temp),rh=mean(RH),temp.se=se(Temp),rh.se=se(RH))
# means <- data.frame(means)
# 
# 
# envs <- data.frame(swc=end.census[,"swc"],nutrients.vars[,c("N","P","K")], means[,c("temp","rh")])
# 
# ## hellinger transformation
# community.trans <- decostand(community[,12:42], "hellinger")
# 
# ## rda with environmental variables
# rda1 <- rda(community.trans, envs)
# anova(rda1)
# (R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 25.7% of variation explained
# 
# 
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda1, type="n", xlim=c(-1,1))
# 
# with(community, levels(Site))
# 
# spp.priority <- colSums(community[,12:42])
# 
# colvec <- c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2")
# with(community, points(rda1, display = "sites", col = "black", pch = c(21,22), bg = colvec[Site]))
# orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=1,)
# text(rda1,  display = "bp", col = "blue", cex = 0.8) 
# legend("bottomright", pch=c(21), legend=community$Site[community$Microsite=="shrub"], pt.bg=colvec, cex=1)
# 
# ## check variable explanation
# vif.cca(rda1)
# 
# v.clim <- cbind(envs[,c("temp","rh")])
# v.nut <- cbind(envs[,c("N","P","K")])
# v.swc <- envs[,"swc"]
# 
# x <- varpart(community.trans,v.clim,v.nut,v.swc)
# 
# showvarparts(3)

Difference in effect size

library(bootES)
## Loading required package: boot
## 
## Attaching package: 'boot'
## The following object is masked from 'package:lattice':
## 
##     melanoma
effect.cal <- function(x){ bootES(x, R=999, data.col="Biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}

effect.site <- by(subset(comm2016, Site != "Barstow"), comm2016$Site[comm2016$Site != "Barstow"], FUN=effect.cal)

site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa)) 
site.summary[5,] <- 0
site.summary <- sapply(site.summary, as.numeric)

par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),as.numeric(site.summary[,"ci.high"]),c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)

## phytometer

effect.cal <- function(x){ bootES(x, R=999, data.col="phyto.biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}

census2016 <- subset(census, Census=="end" & Year ==2016)

effect.site <- by(subset(census2016, Site != "Barstow"), census2016$Site[census2016$Site != "Barstow"], FUN=effect.cal)

site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa)) 
site.summary <- sapply(site.summary, as.numeric)

par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),site.summary[,"ci.high"],c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)

Rii between years

rii <- function(x, j, var)
{
s1 <- subset(x, Microsite == "shrub", select=var)
o1 <- subset(x, Microsite == "open", select=var)
return1 <- (s1 - o1) / (s1+o1)
x1 <- x[seq(1, nrow(x), by = 2),]
return2 <- cbind(x1[j], return1)
return2[is.na(return2)] <- 0
print(return2)
}


rii.dat <- rii(community, j=1:8, var=c("Abundance","Richness","Biomass"))
##     Year  ID            Site Rep      Lat      Long Elevation Gradient
## 1   2017   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 3   2017   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 5   2017   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 7   2017   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 9   2017   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 11  2017   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 13  2017   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 15  2017   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 17  2017   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 19  2017  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 21  2017  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 23  2017  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 25  2017  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 27  2017  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 29  2017  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 31  2017  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 33  2017  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 35  2017  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 37  2017  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 39  2017  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 41  2017  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 43  2017  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 45  2017  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 47  2017  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 49  2017  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 51  2017  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 53  2017  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 55  2017  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 57  2017  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 59  2017  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 61  2017  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 63  2017  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 65  2017  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 67  2017  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 69  2017  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 71  2017  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 73  2017  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 75  2017  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 77  2017  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 79  2017  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 81  2017  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 83  2017  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 85  2017  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 87  2017  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 89  2017  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 91  2017  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 93  2017  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 95  2017  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 97  2017  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 99  2017  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 101 2017  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 103 2017  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 105 2017  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 107 2017  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 109 2017  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 111 2017  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 113 2017  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 115 2017  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 117 2017  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 119 2017  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 121 2017  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 123 2017  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 125 2017  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 127 2017  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 129 2017  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 131 2017  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 133 2017  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 135 2017  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 137 2017  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 139 2017  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 141 2017  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 143 2017  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 145 2017  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 147 2017  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 149 2017  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 151 2017  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 153 2017  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 155 2017  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 157 2017  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 159 2017  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 161 2017  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 163 2017  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 165 2017  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 167 2017  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 169 2017  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 171 2017  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 173 2017  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 175 2017  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 177 2017  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 179 2017  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 181 2017  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 183 2017  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 185 2017  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 187 2017  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 189 2017  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 191 2017  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 193 2017  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 195 2017  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 197 2017  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 199 2017 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 201 2017 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 203 2017 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 205 2017 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 207 2017 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 209 2017 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 211 2017 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 213 2017 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 215 2017 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 217 2017 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 219 2017 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 221 2017 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 223 2017 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 225 2017 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 227 2017 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 229 2017 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 231 2017 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 233 2017 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 235 2017 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 237 2017 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 239 2017 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 241 2017 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 243 2017 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 245 2017 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 247 2017 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 249 2017 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 251 2017 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 253 2017 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 255 2017 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 257 2017 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 259 2017 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 261 2017 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 263 2017 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 265 2017 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 267 2017 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 269 2017 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 271 2017 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 273 2017 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 275 2017 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 277 2017 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 279 2017 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 281 2017 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 283 2017 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 285 2017 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 287 2017 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 289 2017 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 291 2017 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 293 2017 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 295 2017 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 297 2017 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 299 2017 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 301 2017 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 303 2017 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 305 2017 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 307 2017 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 309 2017 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 311 2017 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 313 2017 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 315 2017 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 317 2017 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 319 2017 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 321 2017 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 323 2017 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 325 2017 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 327 2017 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 329 2017 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 331 2017 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 333 2017 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 335 2017 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 337 2017 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 339 2017 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 341 2017 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 343 2017 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 345 2017 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 347 2017 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 349 2017 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 351 2017 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 353 2017 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 355 2017 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 357 2017 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 359 2017 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 361 2017 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 363 2017 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 365 2017 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
## 367 2017 184      TejonRanch   4 34.87593 -118.6025 1117.5090        3
## 369 2017 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 371 2017 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 373 2017 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 375 2017 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 377 2017 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 379 2017 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 381 2017 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 383 2017 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 385 2017 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 387 2017 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 389 2017 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 391 2017 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 393 2017 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 395 2017 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 397 2017 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 399 2017 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 401 2017 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 403 2017 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 405 2017 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 407 2017 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 409 2017 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 411 2017 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 413 2017 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 415 2017 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 417 2017 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 419 2017 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
## 421 2016   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 423 2016   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 425 2016   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 427 2016   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 429 2016   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 431 2016   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 433 2016   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 435 2016   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 437 2016   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 439 2016  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 441 2016  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 443 2016  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 445 2016  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 447 2016  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 449 2016  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 451 2016  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 453 2016  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 455 2016  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 457 2016  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 459 2016  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 461 2016  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 463 2016  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 465 2016  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 467 2016  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 469 2016  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 471 2016  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 473 2016  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 475 2016  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 477 2016  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 479 2016  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 481 2016  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 483 2016  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 485 2016  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 487 2016  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 489 2016  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 491 2016  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 493 2016  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 495 2016  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 497 2016  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 499 2016  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 501 2016  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 503 2016  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 505 2016  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 507 2016  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 509 2016  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 511 2016  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 513 2016  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 515 2016  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 517 2016  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 519 2016  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 521 2016  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 523 2016  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 525 2016  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 527 2016  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 529 2016  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 531 2016  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 533 2016  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 535 2016  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 537 2016  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 539 2016  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 541 2016  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 543 2016  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 545 2016  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 547 2016  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 549 2016  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 551 2016  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 553 2016  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 555 2016  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 557 2016  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 559 2016  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 561 2016  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 563 2016  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 565 2016  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 567 2016  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 569 2016  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 571 2016  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 573 2016  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 575 2016  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 577 2016  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 579 2016  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 581 2016  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 583 2016  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 585 2016  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 587 2016  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 589 2016  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 591 2016  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 593 2016  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 595 2016  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 597 2016  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 599 2016  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 601 2016  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 603 2016  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 605 2016  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 607 2016  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 609 2016  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 611 2016  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 613 2016  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 615 2016  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 617 2016  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 619 2016 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 621 2016 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 623 2016 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 625 2016 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 627 2016 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 629 2016 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 631 2016 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 633 2016 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 635 2016 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 637 2016 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 639 2016 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 641 2016 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 643 2016 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 645 2016 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 647 2016 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 649 2016 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 651 2016 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 653 2016 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 655 2016 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 657 2016 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 659 2016 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 661 2016 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 663 2016 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 665 2016 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 667 2016 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 669 2016 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 671 2016 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 673 2016 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 675 2016 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 677 2016 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 679 2016 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 681 2016 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 683 2016 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 685 2016 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 687 2016 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 689 2016 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 691 2016 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 693 2016 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 695 2016 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 697 2016 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 699 2016 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 701 2016 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 703 2016 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 705 2016 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 707 2016 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 709 2016 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 711 2016 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 713 2016 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 715 2016 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 717 2016 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 719 2016 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 721 2016 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 723 2016 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 725 2016 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 727 2016 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 729 2016 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 731 2016 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 733 2016 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 735 2016 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 737 2016 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 739 2016 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 741 2016 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 743 2016 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 745 2016 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 747 2016 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 749 2016 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 751 2016 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 753 2016 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 755 2016 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 757 2016 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 759 2016 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 761 2016 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 763 2016 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 765 2016 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 767 2016 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 769 2016 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 771 2016 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 773 2016 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 775 2016 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 777 2016 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 779 2016 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 781 2016 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 783 2016 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 785 2016 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
## 787 2016 184      TejonRanch   4 34.87593 -118.6025 1117.5090        3
## 789 2016 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 791 2016 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 793 2016 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 795 2016 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 797 2016 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 799 2016 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 801 2016 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 803 2016 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 805 2016 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 807 2016 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 809 2016 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 811 2016 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 813 2016 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 815 2016 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 817 2016 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 819 2016 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 821 2016 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 823 2016 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 825 2016 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 827 2016 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 829 2016 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 831 2016 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 833 2016 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 835 2016 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 837 2016 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 839 2016 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
##        Abundance    Richness       Biomass
## 1    0.116161616 -0.50000000  0.7119778644
## 3    0.087818697 -0.60000000  0.4796531205
## 5    0.080906149 -0.33333333  0.6615644834
## 7   -0.121568627 -0.50000000  0.2712539822
## 9    0.049853372 -0.50000000  0.4593215503
## 11   0.184357542 -0.50000000  0.6795536250
## 13   0.261780105 -0.50000000  0.4608192877
## 15  -0.252427184  0.00000000  0.4919018585
## 17  -0.174887892 -0.33333333  0.6526389444
## 19   0.014423077 -0.50000000  0.5086644867
## 21   0.139534884 -0.33333333  0.3631851602
## 23   0.210653753 -0.50000000  0.5080463308
## 25  -0.780104712  0.00000000  0.4349842452
## 27   0.311827957 -0.50000000  0.7702038806
## 29   0.336708861 -0.60000000  0.2323219991
## 31   0.392670157 -0.50000000  0.3712568032
## 33   0.110526316 -0.33333333  0.2790786494
## 35   0.290322581 -0.33333333  0.8636516386
## 37   0.111111111 -0.33333333  0.6788779257
## 39   0.144508671 -0.33333333  0.7595415671
## 41   0.412300683 -0.50000000  0.8449457768
## 43   0.419354839 -0.50000000 -0.1049540415
## 45   0.357798165 -0.50000000  0.6227795285
## 47   0.219858156 -0.33333333 -0.0902199449
## 49   0.330000000 -0.33333333  0.6187907821
## 51   0.126760563 -0.33333333  0.7574940805
## 53   0.248908297 -0.50000000  0.5044125134
## 55   0.040767386 -0.50000000  0.5665006524
## 57   0.323456790  0.00000000  0.7189177620
## 59   0.307462687  0.00000000  0.4426137567
## 61   0.475504323 -0.66666667  0.6770310149
## 63   0.223529412 -0.33333333  0.8100986434
## 65   0.444743935 -0.20000000  0.6336056940
## 67  -0.023529412 -0.50000000  0.7814083673
## 69   0.191637631 -0.60000000 -0.0870758305
## 71  -0.360655738 -0.20000000  0.1184504218
## 73   0.238434164  0.00000000  0.8905726334
## 75   0.341772152 -0.60000000  0.9400286944
## 77  -0.789473684 -0.50000000  0.7695158398
## 79   0.235955056 -0.20000000  0.5964276417
## 81   0.318750000 -0.50000000  0.4039094315
## 83  -0.035087719 -0.20000000  0.4961624763
## 85  -0.100671141 -0.20000000  0.4602290313
## 87  -0.107142857 -0.20000000  0.4550573406
## 89   0.077844311 -0.50000000  0.5893232187
## 91   0.275362319 -0.20000000  0.7070642723
## 93  -0.166666667  0.00000000  0.4056391915
## 95   0.575757576 -0.20000000  0.8496008659
## 97  -0.105882353  0.00000000  0.4560603999
## 99  -0.185185185 -0.60000000  0.3895348185
## 101  0.166666667 -0.33333333  0.6450904620
## 103  0.012448133 -0.60000000  0.5448609160
## 105  0.026548673 -0.50000000  0.5547202132
## 107 -0.362637363 -0.33333333  0.2152793275
## 109 -0.083969466 -0.33333333  0.4733767791
## 111 -0.041420118 -0.50000000  0.5058660462
## 113 -0.178294574 -0.50000000  0.3955781995
## 115 -0.465648855  0.00000000  0.0938392832
## 117  0.292134831 -0.60000000  0.7160642623
## 119  0.300000000 -0.20000000  0.7202401148
## 121 -0.069767442  0.00000000  0.8483307317
## 123  0.670886076  0.50000000  0.8016949153
## 125 -0.258064516  0.50000000  0.8142623553
## 127  0.400000000 -0.20000000  0.7902743020
## 129 -0.312500000  0.50000000  0.4274566970
## 131 -0.052631579 -0.33333333  0.5074892105
## 133 -0.368421053  0.00000000  0.4073927026
## 135 -0.037037037  0.33333333  0.6696825136
## 137  0.222222222  0.20000000  0.4387597894
## 139 -0.179487179  0.00000000  0.2695463320
## 141  0.312500000  0.00000000  0.4541074440
## 143 -0.028571429 -0.50000000  0.1521536453
## 145 -0.777777778  0.00000000  0.5596072931
## 147  0.076923077  0.00000000  0.8679251279
## 149 -0.272727273 -0.20000000  0.7130066743
## 151 -0.150000000  0.00000000  0.5380087283
## 153 -0.125000000 -0.50000000  0.9397717963
## 155 -0.034482759  0.33333333  0.4481977343
## 157 -0.153846154  0.20000000  0.3940984309
## 159 -0.161290323  0.14285714  0.6146335434
## 161 -0.021276596 -0.33333333  0.2152743275
## 163  0.333333333  0.00000000  0.7832666333
## 165 -0.034482759  0.00000000  0.6498431394
## 167  0.142857143  0.20000000  0.8402163451
## 169 -0.476190476 -0.14285714  0.3567608862
## 171 -0.076923077  0.33333333  0.6519212141
## 173 -0.111111111  0.00000000  0.2074660006
## 175  0.000000000  0.50000000  0.8967097043
## 177  0.111111111  0.33333333  0.9217960271
## 179 -0.448275862 -0.50000000 -0.5522570506
## 181  0.411764706 -0.33333333  0.8533612299
## 183  1.000000000  1.00000000  1.0000000000
## 185  0.333333333  0.20000000  0.5700227101
## 187  0.222222222  0.00000000  0.1455301455
## 189  0.310344828 -0.25000000  0.2102272727
## 191  0.714285714  0.60000000  0.6671493587
## 193  0.172413793 -0.20000000  0.0506990062
## 195  0.391304348 -0.14285714  0.7011118378
## 197 -0.111111111 -0.14285714  0.2945083620
## 199  0.000000000  0.20000000  0.9061425061
## 201 -0.083333333  0.00000000  0.3469970429
## 203  0.285714286  0.00000000  0.7982496545
## 205  0.000000000  0.14285714 -0.1149301826
## 207  1.000000000  1.00000000  1.0000000000
## 209  0.304347826  0.00000000  0.7495439299
## 211  0.294117647  0.20000000  0.7542619910
## 213  0.272727273  0.33333333  0.8833221251
## 215  0.176470588  0.60000000  0.8143354903
## 217  0.214285714 -0.14285714  0.8192456119
## 219 -0.142857143  0.14285714  0.3285299495
## 221  0.354838710  0.50000000 -0.2584942085
## 223  0.081081081  0.00000000  0.0354693197
## 225  0.058823529 -0.33333333  0.5296251511
## 227  1.000000000  1.00000000  1.0000000000
## 229  0.083333333 -0.20000000  0.8066840601
## 231  1.000000000  1.00000000  1.0000000000
## 233  1.000000000  1.00000000  1.0000000000
## 235  1.000000000  1.00000000  1.0000000000
## 237  0.250000000  0.33333333  0.6100965831
## 239  1.000000000  1.00000000  1.0000000000
## 241  0.461538462  0.20000000  0.4317400039
## 243  0.120000000  0.00000000 -0.0587628553
## 245 -0.360000000  0.00000000  0.6923697110
## 247  0.750000000  0.42857143  0.6393058945
## 249  0.777777778  0.60000000  0.7798188223
## 251 -0.120000000  0.00000000  0.4696642911
## 253  1.000000000  1.00000000  1.0000000000
## 255 -0.272727273 -0.20000000  1.0000000000
## 257  1.000000000  1.00000000  0.9201925608
## 259 -0.333333333  0.50000000  0.5734497079
## 261  0.000000000  0.00000000  1.0000000000
## 263  1.000000000  1.00000000  1.0000000000
## 265  0.391304348  0.11111111  0.7209858818
## 267  0.285714286 -0.14285714 -0.3734034560
## 269  0.166666667  0.14285714  0.7327057183
## 271  0.120000000  0.14285714  0.3756266122
## 273  0.000000000  0.00000000  0.0000000000
## 275  1.000000000  1.00000000  1.0000000000
## 277  0.818181818  0.33333333  0.4608250720
## 279  0.466666667 -0.14285714  0.6183624064
## 281  0.428571429  0.00000000  0.7515356072
## 283  0.176470588 -0.20000000  0.8261728100
## 285  0.428571429  0.20000000  0.9261516655
## 287  0.428571429  0.14285714  0.9148376772
## 289  0.333333333  0.00000000  0.1529029029
## 291  0.272727273  0.33333333  0.4494782901
## 293  0.555555556  0.20000000  0.3213861292
## 295  0.833333333  0.50000000  0.7620473727
## 297  0.529411765  0.33333333  0.7677135242
## 299 -0.466666667 -0.20000000 -0.0003025352
## 301  0.541666667  0.00000000  0.8890477983
## 303  0.285714286  0.33333333  0.5099159664
## 305  1.000000000  1.00000000 -0.3159687437
## 307  0.478260870  0.00000000 -0.0193116226
## 309  0.548387097 -0.33333333 -0.7561455261
## 311  0.488372093  0.33333333  0.6375491617
## 313 -0.170731707 -0.33333333  0.7590221187
## 315  0.297297297 -0.33333333  0.8505929820
## 317  0.085714286  0.00000000  0.5696080789
## 319  0.265306122  0.00000000  0.9412910878
## 321  0.416666667  0.00000000  0.6330641418
## 323  0.351351351  0.00000000  0.9117237890
## 325  0.032258065 -0.33333333  0.6542429594
## 327 -0.176470588 -0.33333333  0.9460873064
## 329 -0.333333333  0.20000000  0.4748446582
## 331  0.176470588  0.00000000  0.2407950472
## 333  0.428571429  0.00000000  0.7813517136
## 335  0.106382979  0.20000000  0.6535572249
## 337  0.219512195  0.00000000  0.0622276029
## 339  0.106382979  0.20000000  0.8631557122
## 341  0.243243243  0.20000000  0.6847489888
## 343  0.733333333  0.00000000  0.8604977217
## 345  0.657142857 -0.50000000  0.9009414654
## 347  0.428571429  0.20000000  0.1832829809
## 349  0.428571429 -0.20000000  0.8294730575
## 351  0.750000000  0.00000000  0.9396562233
## 353 -0.310344828  0.20000000  0.5641348440
## 355  0.166666667 -0.20000000  0.8345676199
## 357  0.288888889 -0.20000000 -0.5533338128
## 359  0.345454545  0.20000000  0.7327935223
## 361  0.343750000  0.20000000  0.1179597085
## 363  0.085714286  0.00000000  0.6375540134
## 365  0.565217391  0.00000000  0.1778108927
## 367 -0.223529412  0.00000000 -0.7049122704
## 369  0.302325581 -0.14285714  0.7018751576
## 371  0.262135922 -0.20000000  0.5240219453
## 373  0.426470588 -0.20000000  0.9176670543
## 375  0.421052632  0.14285714  0.9266820402
## 377  0.194444444 -0.11111111  0.6235886798
## 379  0.353535354 -0.33333333  0.9160009368
## 381  0.223529412 -0.25000000  0.4068392907
## 383  0.111111111 -0.20000000  0.9252881544
## 385  0.290322581  0.00000000  0.4647619048
## 387  0.102564103  0.00000000  0.1074135091
## 389  0.596153846  0.00000000  0.6919047329
## 391  0.438596491 -0.14285714  0.5588887903
## 393  0.095238095  0.25000000  0.9118715622
## 395  0.127272727 -0.14285714  0.4872740419
## 397 -0.214285714 -0.25000000  0.5206069618
## 399  0.093525180  0.20000000  0.7185471220
## 401  0.360000000  0.00000000  0.5236393303
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## 405  0.145454545  0.00000000  0.3372343362
## 407  0.418604651  0.00000000  0.5719982574
## 409  0.405405405  0.00000000  0.5611774811
## 411 -0.009708738 -0.33333333  0.1923509561
## 413  0.428571429 -0.11111111  0.5800751438
## 415 -0.183098592 -0.20000000  0.0192870201
## 417  0.220000000 -0.11111111  0.4037345143
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## 437 -0.155963303 -0.20000000  0.6342772166
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## 447  0.434944238  0.00000000  0.7276661111
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## 453 -0.085501859 -0.33333333  0.2083004494
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## 457  0.135802469  0.00000000  0.7399925355
## 459  0.067669173 -0.33333333  0.7541348051
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## 463 -0.087281796 -0.20000000 -0.1555443792
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## 467  0.147982063  0.00000000 -0.0252802706
## 469  0.092783505  0.00000000  0.6221160673
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## 481 -0.469387755 -0.42857143 -0.7142306828
## 483 -0.146341463  0.14285714 -0.5397365232
## 485 -0.046153846 -0.14285714  0.2602530238
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## 523 -0.263157895 -0.14285714 -0.2207533760
## 525 -1.000000000 -1.00000000 -0.0044416878
## 527 -0.672727273 -0.50000000 -0.4692221884
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## 533 -0.391304348 -0.20000000 -0.1611568092
## 535  0.461538462  0.25000000  0.3753283347
## 537 -0.521739130 -0.20000000  0.5213391895
## 539 -0.395348837 -0.66666667  0.5102448342
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## 575  0.000000000  0.00000000  0.0000000000
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## 583  0.000000000  0.00000000  0.0000000000
## 585  0.000000000  0.00000000  0.0000000000
## 587  0.000000000  0.00000000  0.0000000000
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## 599  0.000000000  0.00000000  0.0000000000
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## 603 -0.714285714 -0.50000000 -0.6126435068
## 605  0.000000000  0.00000000 -0.5626066874
## 607  0.636363636  0.33333333  0.0541506322
## 609  0.444444444  0.00000000  0.3603976342
## 611  0.428571429  0.20000000  0.1440466750
## 613  0.100000000  0.11111111 -0.1163394391
## 615  0.666666667  0.60000000  0.3524324736
## 617 -0.166666667 -0.25000000  0.3681481097
## 619 -0.333333333 -0.25000000 -0.6793068477
## 621 -0.090909091  0.25000000 -0.5180089771
## 623  0.368421053  0.00000000  0.1999727712
## 625 -0.500000000  0.00000000 -0.3983889528
## 627  0.416666667  0.00000000  0.2348139481
## 629 -0.500000000  0.00000000  0.1677600750
## 631 -0.222222222 -0.09090909  0.2765970257
## 633 -0.066666667 -0.11111111 -0.2515765549
## 635  0.750000000  0.71428571 -0.2304842213
## 637  0.076923077  0.20000000  0.4736807388
## 639  0.217391304  0.00000000  0.3278188942
## 641  0.500000000  0.33333333  0.5121937686
## 643  0.428571429  0.00000000  0.5644350976
## 645  0.818181818  0.33333333  0.7509420420
## 647  0.000000000  0.20000000  0.0419393939
## 649  0.600000000  0.50000000 -0.2105005707
## 651  1.000000000  1.00000000  1.0000000000
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## 655  1.000000000  1.00000000  1.0000000000
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## 667 -0.500000000  0.00000000 -0.1854517901
## 669  0.000000000  0.00000000  0.0000000000
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## 673  1.000000000  1.00000000  1.0000000000
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## 679 -1.000000000 -1.00000000 -1.0000000000
## 681  1.000000000  1.00000000  1.0000000000
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## 691  0.333333333  0.33333333  0.0359679267
## 693  1.000000000  1.00000000  1.0000000000
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## 697 -0.200000000  0.00000000  0.2269216624
## 699 -1.000000000 -1.00000000 -1.0000000000
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## 709  0.000000000  0.00000000  0.0000000000
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## 715  1.000000000  1.00000000  1.0000000000
## 717  0.000000000  0.00000000  0.0000000000
## 719  0.000000000  0.00000000  0.0000000000
## 721  0.377358491  0.00000000  0.3484005285
## 723  0.505154639 -0.20000000 -0.0318575779
## 725  0.312500000  0.00000000  0.6159210920
## 727  0.441860465  0.20000000  0.5693343899
## 729 -0.122807018  0.00000000  0.0441698012
## 731  0.473684211 -0.20000000  0.4318516005
## 733  0.489361702  0.33333333 -0.5605433282
## 735 -0.045454545  0.00000000  0.8379392199
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## 739 -0.186440678 -0.33333333  0.4705570292
## 741  0.224489796  0.00000000  0.4530265803
## 743  0.261538462  0.00000000  0.4337169160
## 745 -0.044776119  0.20000000  0.4990751910
## 747 -0.219512195 -0.33333333  0.5602957031
## 749  0.310344828  0.00000000  0.9537213059
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## 755  0.244444444 -0.33333333  0.5423092289
## 757 -0.125000000 -0.60000000  0.2539237668
## 759  0.346938776 -0.66666667 -0.1012083626
## 761  0.289473684  0.00000000  0.1958249759
## 763  0.257142857 -0.20000000  0.0970992480
## 765  0.192982456 -0.33333333  0.7428781261
## 767 -0.236363636  0.00000000  0.8792069086
## 769 -0.128205128 -0.20000000  0.1232449298
## 771  0.407407407  0.00000000  0.6149531455
## 773  0.132075472 -0.60000000  0.7241809842
## 775  0.317073171 -0.20000000  0.2104357140
## 777 -0.120000000  0.20000000 -0.1236287160
## 779 -0.160000000  0.33333333  0.4350836408
## 781  0.382716049  0.20000000 -0.0125921551
## 783  0.303030303  0.00000000 -0.0013919268
## 785 -0.062500000  0.33333333  0.1931782458
## 787 -0.333333333  0.14285714 -0.8667356779
## 789  0.138461538  0.25000000  0.4275430035
## 791  0.000000000  0.33333333  0.6825802976
## 793  0.163636364  0.14285714  0.5992973553
## 795 -0.222222222 -0.20000000 -0.0999432785
## 797  0.000000000 -0.11111111 -0.6727514596
## 799  0.161290323 -0.11111111  0.6025874346
## 801  0.196581197 -0.14285714 -0.4921950789
## 803  0.015384615  0.14285714 -0.0394353574
## 805 -0.475409836  0.20000000 -0.4762100212
## 807  0.170731707  0.25000000 -0.3190357440
## 809  0.052631579  0.00000000  0.7788296402
## 811  0.093525180 -0.09090909 -0.5659009699
## 813  0.156626506  0.25000000 -0.2578781513
## 815  0.160493827 -0.14285714  0.0833863781
## 817 -0.217391304 -0.42857143  0.5182010956
## 819  0.373134328  0.33333333  0.2026123137
## 821  0.250000000 -0.42857143 -0.1397175313
## 823 -0.345454545 -0.20000000  0.3225661732
## 825 -0.153846154 -0.14285714 -0.5790226460
## 827  0.243902439  0.33333333 -0.1462200957
## 829 -0.075000000 -0.50000000  0.5087807832
## 831 -0.254901961  0.11111111  0.3546859422
## 833  0.225806452  0.00000000  0.6949414737
## 835  0.026315789  0.33333333  0.0135630499
## 837  0.111111111  0.14285714 -0.0244374546
## 839 -0.016949153  0.00000000  0.2277740481
rii.mean <- rii.dat %>% group_by(Year, Gradient, Site) %>% summarize(avg=mean(Biomass),se=se(Biomass))
rii.mean <- data.frame(rii.mean)

plot(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"], ylim=c(-1,1), pch=19, cex=1.5, xlim=c(0.5,7.5), ylab="Rii Biomass", xlab="Site", xaxt="n", cex.lab=1.5)
axis(1, 1:7, lab=unique(rii.mean$Site))
error.bar(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"],rii.mean[rii.mean$Year==2016,"se"])
error.bar(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"],rii.mean[rii.mean$Year==2017,"se"])
points(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"], pch=21, bg="Grey50", cex=1.5)
abline(h=0, lwd=2, lty=2)
legend(6.6,-0.65, c("2016","2017"), pch=22, pt.bg=c("Black","Grey50"), cex=1.6)

## add year column to precipitation data
precip[,"Year"] <- ifelse(precip$season=="season.1","2016","2017")
rii.precip <- merge(precip,rii.mean, by=c("Year","Gradient"))



ihs <- function(x) {
    y <- log(x + sqrt(x ^ 2 + 1))
    return(y)
}

## Precipitation vs Rii
plot(log(rii.precip[,"Precip"]), rii.precip[,"avg"], ylab= "RII", xlab="log precipitation Precipitation", pch=19, cex=1.5, cex.lab=1.5, cex.axis=1.3)
abline(h=0, lwd=2, lty=2)

m1 <- lm(rii.precip[,"avg"] ~ rii.precip[,"Precip"])